Patient state determination based on one or more spectral characteristics of a bioelectrical brain signal

ABSTRACT

In some examples, a processor determines a patient state based on activity of a bioelectrical brain signal of a patient in one or more frequency sub-bands of a frequency band of interest. For example, a processor may determine a patient state based on the power level of a bioelectrical brain signal of the patient in one or more frequency sub-bands of a frequency band, or based on a spectral pattern of a bioelectrical brain signal in a frequency band, such as a shift in a power distribution between sub-bands, a change in the peak frequency within one or more sub-bands, a pattern of the power distribution over one or more frequency sub-bands, or a width or a variability of one or more sub-bands exhibiting a relatively high or low level of activity.

This application is a continuation of U.S. patent application Ser. No.13/955,367, filed on Jul. 31, 2013, which claims the benefit of U.S.Provisional Application No. 61/831,014, filed on Jun. 4, 2013 andentitled “PATIENT STATE DETERMINATION BASED ON ONE OR MORE SPECTRALCHARACTERISTICS OF A BIOELECTRICAL BRAIN SIGNAL,” the entire content ofeach of which is incorporated herein by reference.

TECHNICAL FIELD

The disclosure relates to patient monitoring with a medical device.

BACKGROUND

Medical devices, such as electrical stimulators or therapeutic agentdelivery devices, may be used in different therapeutic applications,such as deep brain stimulation (DBS), spinal cord stimulation (SCS),pelvic stimulation, gastric stimulation, peripheral nerve stimulation,functional electrical stimulation or delivery of pharmaceutical agent,insulin, pain relieving agent or anti-inflammatory agent to a targettissue site within a patient. A medical device may be configured todeliver therapy to a patient to treat a variety of symptoms or patientconditions such as chronic pain, tremor, Parkinson's disease, othertypes of movement disorders, seizure disorders (e.g., epilepsy), urinaryor fecal incontinence, sexual dysfunction, obesity, mood disorders,gastroparesis or diabetes. In some therapy systems, an electricalstimulator, which may be implantable in some instances, deliverselectrical therapy to a target tissue site within a patient with the aidof one or more electrodes, which may be deployed by medical leads, on ahousing of the electrical stimulator, or both. In addition to or insteadof electrical stimulation therapy, a medical device, which may beimplantable in some instances, may deliver a therapeutic agent to atarget tissue site within a patient with the aid of one or more fluiddelivery elements, such as a catheter or a therapeutic agent elutingpatch.

Some medical devices are configured to sense a patient parameter, suchas a bioelectrical brain signal. A sensed patient parameter may be usedfor various purposes, such as to control therapy delivery by a medicaldevice.

SUMMARY

The disclosure describes example systems, devices, and methods fordetermining a patient state based on activity of a bioelectrical brainsignal of a patient in one or more frequency sub-bands of a frequencyband of interest. The activity of a bioelectrical brain signal in aparticular frequency sub-band may be indicated by, for example, thesignal strength (also referred to herein as “power,” “power level,” or“spectral amplitude”) in the frequency sub-band. In some examplesdescribed herein, a patient state is determined based on the power levelof a bioelectrical brain signal of the patient in one or more frequencysub-bands of a frequency band. The patient state can be, for example, apatient disease state, a state in which a symptom of a patient conditionis observed, or a patient state indicative of the efficacy of therapydelivered by a medical device or the efficacy of medication.

In addition, or instead, in some examples, a patient state is determinedbased on a spectral pattern of a bioelectrical brain signal in thefrequency band of interest, such as a shift in a power distributionbetween sub-bands of the frequency band (e.g., a change over time of thefrequency sub-band in which a peak power level of the bioelectricalbrain signal or a peak power within a frequency band of interest isobserved), a change in the peak power level within one or more frequencysub-bands, a pattern of the power distribution of a sensed bioelectricalbrain signal over one or more frequency sub-bands (e.g., whether a plotof the power level versus frequency illustrates a narrow peak, a broadpeak, a unimodal peak, or a bimodal peak), or a width or a variability(e.g., in the width) of one or more frequency sub-bands exhibiting arelatively high or low level of activity.

Different frequency bands of a bioelectrical brain signal may beassociated with different brain activity of the patient. The brainactivity may be, for example, associated with a patient-initiated state(e.g., e.g., a movement state, a speech state, or a sleep state), apatient condition (e.g., a disease state), or the occurrence of aspecific symptom of a patient condition. Example frequency bands includethe delta band, alpha band, beta band, gamma band, and high gamma band.A frequency band may include a plurality of frequency sub-bands, whicheach have a width that is narrower than the frequency band. Thefrequency band may be defined by a plurality of frequency sub-bands.Brain activity within one or more particular frequency sub-bands, asopposed to activity in the broader frequency band itself, may beindicative of particular patient states. The activity within a frequencysub-band can be indicated by a power level (or amplitude) within thefrequency sub-band.

In some examples, a processor determines a patient state based onactivity of a bioelectrical brain signal of a patient in one or morefrequency sub-bands of a frequency band of interest and generates anindication of the determined patient state. The processor may controltherapy delivery to the patient based on the determined patient state,monitor a patient condition based on the determined patient state,generate a patient diagnosis (e.g., determines a patient conditionsub-type) based on the determined patient state, or any combinationthereof.

In some examples, the disclosure describes example systems, devices, andmethods for determining whether a patient has Parkinson's disease oranother patient condition based on activity of a bioelectrical brainsignal of a patient in one or more frequency sub-bands of a frequencyband of interest. In some cases, bioelectrical brain signals of patientswithout Parkinson's disease may not exhibit certain activity in one ormore frequency sub-bands of a frequency band of interest, whereasbioelectrical brain signals of patients with Parkinson's disease mayexhibit certain activity in one or more frequency sub-bands of afrequency band of interest. Thus, this activity in one or more frequencysub-bands of a frequency band of interest may be indicative of thepresence of Parkinson's disease and may, therefore, be used in someexamples to diagnose Parkinson's disease.

In one example, the disclosure is directed to a method that comprisesreceiving, with one or more processors, information representative of abioelectrical brain signal of a patient, determining, with the one ormore processors, a patient state based on activity of the bioelectricalbrain signal within one or more frequency sub-bands of a frequency bandof the bioelectrical brain signal, and generating, with the one or moreprocessors, an indication of the determined patient state.

In another example, the disclosure is directed to a system thatcomprises a sensing module configured to sense a bioelectrical brainsignal of a patient, and one or more processors configured to determinea patient state based on activity of the bioelectrical brain signalwithin one or more frequency sub-bands of a frequency band of thebioelectrical brain signal, and generate an indication of the determinedpatient state.

In another example, the disclosure is directed to a system thatcomprises means for sensing a bioelectrical brain signal of a patient,and means for determining a patient state based on activity of thebioelectrical brain signal within one or more frequency sub-bands of afrequency band of the bioelectrical brain signal.

In another aspect, the disclosure is directed to a computer-readablemedium containing instructions that, when executed by one or moreprocessors, cause the one or more processors to receive informationrepresentative of a bioelectrical brain signal of a patient, anddetermine a patient state based on activity of the bioelectrical brainsignal within one or more frequency sub-bands of a frequency band of thebioelectrical brain signal.

In one example, the disclosure is directed to a method that comprisesreceiving, with one or more processors, information representative of abioelectrical brain signal of a patient, and determining, with the oneor more processors, a biomarker indicative of a patient state, whereindetermining the biomarker comprises determining a characteristic of thebioelectrical brain signal within one or more frequency sub-bands of afrequency band of the bioelectrical brain signal indicative of thepatient state.

In another example, the disclosure is directed to a system thatcomprises a sensing module configured to sense a bioelectrical brainsignal of a patient, and one or more processors configured to determinea biomarker indicative of a patient state by at least determining acharacteristic of the bioelectrical brain signal within one or morefrequency sub-bands of a frequency band of the bioelectrical brainsignal indicative of the patient state.

In another example, the disclosure is directed to a system thatcomprises means for sensing a bioelectrical brain signal of a patient,and means for determining a biomarker indicative of a patient state,wherein the biomarker comprises a characteristic of the bioelectricalbrain signal within one or more frequency sub-bands of a frequency bandof the bioelectrical brain signal indicative of the patient state.

In another aspect, the disclosure is directed to a computer-readablemedium containing instructions that, when executed by one or moreprocessors, cause the one or more processors to receive informationrepresentative of a bioelectrical brain signal of a patient, anddetermine a biomarker indicative of a patient state, wherein thebiomarker comprises a characteristic of the bioelectrical brain signalwithin one or more frequency sub-bands of a frequency band of thebioelectrical brain signal indicative of the patient state.

In another aspect, the disclosure is directed to a computer-readablestorage medium, which may be an article of manufacture. Thecomputer-readable storage medium includes computer-readable instructionsfor execution by one or more processors. The instructions cause one ormore processors to perform any part of the techniques described herein.The instructions may be, for example, software instructions, such asthose used to define a software or computer program.

The details of one or more examples are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages will be apparent from the description and drawings, and fromthe claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual diagram illustrating an example deep brainstimulation (DBS) system configured to sense a bioelectrical brainsignal and deliver electrical stimulation therapy to a tissue sitewithin a brain of a patient.

FIG. 2 is functional block diagram illustrating components of an examplemedical device.

FIG. 3 is a functional block diagram illustrating components of anexample medical device programmer.

FIG. 4 is a flow diagram illustrating an example technique fordetermining a patient state based on a sensed bioelectrical brainsignal.

FIG. 5 is an example spectrogram of a bioelectrical brain signal sensedwithin a brain of a human subject.

FIGS. 6A and 6B are example spectrograms of bioelectrical brain signalssensed within brains of human subjects, and illustrate the effects ofmedication on activity of the bioelectrical brain signals.

FIGS. 7A-7D illustrate the power spectra of local field potentials(LFPs) recorded from electrodes implanted in brains of human subjectsdiagnosed with Parkinson's disease.

FIG. 8A is a graph that illustrates characteristics of the beta bandactivity of a LFP sensed within a basal ganglia of a brain of a humanpatient over an approximately 24 hour period of time, and, inparticular, illustrates the peak spectral amplitude of the beta bandactivity and the corresponding frequency at which the peak spectralamplitude occurred.

FIG. 8B illustrates a histogram of the beta band frequencies shown inFIG. 8A corresponding to the peak spectral amplitudes.

FIG. 9 is a flow diagram illustrating an example technique fordetermining one or more biomarkers indicative of a particular patientstate.

FIG. 10 illustrates a plurality of graphs that each indicates the betaband activity of a LFP sensed within brains of human subjects.

DETAILED DESCRIPTION

The disclosure describes example systems, devices, and methods fordetermining a patient state based on one or more frequency domaincharacteristics of a bioelectrical brain signal of a patient, and, inparticular activity in one or more frequency sub-bands of a frequencyband of interest. The activity of a bioelectrical brain signal in aparticular frequency sub-band may be indicated by, for example, thesignal strength (also referred to herein as “power,” “power level,” or“spectral amplitude”) in the frequency sub-band. Thus, a peak powerlevel within a frequency band may be, for example, the greatest signalstrength in the frequency band. The peak power level of a sensedbioelectrical brain signal may also be referred to as an “oscillationpeak” in some examples.

The activity of a bioelectrical brain signal of a patient in one or morefrequency sub-bands of a frequency band of interest that may beindicative of a patient state includes, for example, a spectral patternof a bioelectrical brain signal, a power level of a bioelectrical brainsignal in one or more frequency sub-bands (e.g., two or more frequencysub-bands) of a frequency band, or both. The patient state can be, forexample, a patient disease state, a state in which a symptom of apatient condition is observed, or a patient state indicative of theefficacy of therapy delivery by a medical device or the efficacy ofmedication.

Different frequency bands of a bioelectrical brain signal are associatedwith different brain activity of the patient. One example of thefrequency bands is shown in Table 1 below:

TABLE 1 Frequency (f) Band Hertz (Hz) Frequency Information f < 4 Hz δ(delta frequency band) 4 Hz ≤ f < 8 Hz theta frequency band 8 Hz ≤ f <13 Hz α (alpha frequency band) 13 Hz < f < 35 Hz β (beta frequency band)35 Hz ≤ f < 100 Hz γ (gamma frequency band) 100 Hz < f < 400 Hz high γ(high gamma frequency band)

In other examples, however, the frequency bands may have differentfrequency ranges. A frequency band may include (e.g., may be made up of)a plurality of frequency sub-bands, which each have a width that isnarrower than the frequency band. The frequency sub-bands of a frequencyband may have substantially the same (e.g., the same or nearly the same)widths or different widths.

The activity within a particular frequency band of a sensedbioelectrical brain signal may change as a function of one or morepatient states. For example, the activity within a particular frequencyband of a sensed bioelectrical brain signal may be indicative of apatient-initiated state (e.g., a movement state, a speech state, or asleep state) or a patient state indicative of the occurrence of one ormore patient symptoms associated with a patient condition. The patientstate indicated by the activity within a particular frequency band mayor may not be the result of volitional patient activity.

A frequency band, e.g., each of the frequency bands indicated above,includes a plurality of frequency sub-bands, which are each defined by anarrower frequency band than the frequency band. It is believed that theactivity within one or more particular frequency sub-bands, as opposedto activity in the broader frequency band itself, may be betterindicative of a specific patient state than the power level in thebroader frequency band. These specific patient states may, for example,provide a better indication of the progression of a patient condition(e.g., the patient pathology), the therapeutic effect of a particulartherapy (e.g., electrical stimulation therapy or pharmaceuticalmedications), the presence of a symptom of a patient condition, thepresence or absence of a patient condition, or any combination thereof.

While the power level of the bioelectrical brain signal in a particularfrequency band may be useful for determining a patient state (e.g., amovement state, a sleep state, a speech state, or a disease state), insome cases, a patient state determination based on the power level inone or more frequency sub-bands may provide a better specificity of thepatient state and a better granularity of patient state determinations.For example, a movement state may be indicated by changes in not onlythe amplitude of beta band activity of a bioelectrical brain signal, butalso changes in (e.g., increasing or decreasing shifts) in the peakfrequency of beta band activity during movements. This can be indicatedby, for example, the frequency sub-band of the beta band in which thepeak power level within the overall beta band is observed.

The power level of the bioelectrical brain signal in one or morefrequency sub-bands of a frequency band of interest may have a bettercorrelation to specific patient states compared to the total power levelin the frequency band of interest. In this way, the frequency componentsof activity in a frequency band of a bioelectrical brain signal mayindicate a fuller picture of a patient condition than the power level ofin the frequency band alone. The frequency components include, forexample, the power level of the bioelectrical brain signal in one ormore frequency sub-bands of the frequency band.

The more specific patient state determinations made based on the powerlevel in one or more frequency sub-bands of a frequency band of interestmay be useful for monitoring a patient condition (e.g., the progressionof the patient disease state), generating a patient diagnosis (e.g.,determining a presence or absence of a patient condition or determininga patient condition sub-type, such as a particular type of patientcondition or a severity of a particular patient condition), controllingtherapy delivery, or any combination thereof.

It is believed that one or more characteristics of the bioelectricalbrain signal in the one or more frequency sub-bands of a frequency bandof interest may be revealing of specific patient states. For example, itis believed that a spectral pattern of a bioelectrical brain signal maybe revealing of specific patient states. The spectral pattern can beindicated by, for example, the distribution of the signal strength(e.g., as indicated by the power level) over one or more frequencysub-bands of a frequency band of interest or over a plurality offrequency bands of interest. Example spectral patterns that may beindicative of an occurrence of a particular patient state include, forexample, a shift in a power distribution between sub-bands of thefrequency band over time (e.g., a change in the frequency sub-band inwhich a peak power level of the signal across all frequency bands or apeak power within a frequency band of interest is observed, or the ratioof power distributions between two or more frequency sub-bands), achange in the peak power level within one or more frequency sub-bands, apattern of the power distribution over one or more frequency sub-bands(e.g., whether a plot of the power level versus frequency illustrates anarrow peak, a broad peak, a unimodal peak, or a bimodal peak, the peakbeing the peak amplitude within the frequency band or frequencysub-band), a width or a variability (e.g., in the width) of one or morefrequency sub-bands exhibiting a relatively high or low level ofactivity, or other characteristics observed in the distribution of thesignal strength over one or more frequency sub-bands of a frequency bandof interest or over a plurality of frequency bands of interest.

The pattern of the power distribution over one or more frequencysub-bands indicative of a particular patient state can be within thesame frequency band or over one or more frequency bands. The spectralpattern, as well as other frequency domain characteristics of abioelectrical brain signal, may be determined based on any suitabletransform of the sensed bioelectrical brain signal, such as, but notlimited to, a Fast Fourier Transform.

While short duration recordings (e.g., on the order of seconds) of abioelectrical brain signal may indicate the power level within aparticular frequency band of interest, the short duration recordings maynot indicate spectral patterns of the bioelectrical brain signal. Thus,the short duration recordings may not provide a full picture of theprogression of pathological activity of a patient. In contrast,relatively long term recordings (e.g., on the order of minutes, hours,or even days) of a bioelectrical brain signal may reveal spectralpatterns or a change in the peak power level over time that offer abetter picture of the progression of the pathological activity of thepatient.

In some examples disclosed herein, a processor of a device (alone or incombination with another processor) determines a patient state based onactivity of a bioelectrical brain signal of the patient in one or morefrequency sub-bands of a frequency band. For example, the processor maydetermine a patient state based on the power level in one or moreparticular frequency sub-bands of a frequency band, based on thespectral patterns of a bioelectrical brain signal (e.g., a shift indominant power from one frequency sub-band to another over time, achange in the frequency sub-band in which the peak power within thefrequency band is observed, a shape of a plot indicating thedistribution of the signal strength), or any combination thereof. Insome examples, the processor generates an indication of the determinedpatient state, controls therapy delivery to the patient based on thedetermined patient state, monitors a patient condition based on thedetermined patient state, generates a patient diagnosis (e.g.,determines a patient condition sub-type) based on the determined patientstate, or any combination thereof.

During a learning phase, oscillation spectra of a bioelectrical brainsignal sensed in a brain of a patient may be assessed to determinespectral characteristics of a bioelectrical brain signal that areassociated with one or more different patient states. Example patientstates include, but are not limited to, normal brain function, abnormalbrain function, specific patient symptoms, a movement state, a sleepstate, a speech state, or a state in which an activity (e.g., a symptom)that occurs as a result of a patient condition is observed. Examplespectral characteristics include, for example, power levels in one ormore frequency sub-bands, spectral patterns, or any combination thereof.These spectral characteristics may be, for example, stored by a deviceas biomarkers indicative of a particular patient state, such as normalor pathological behaviors, responses to medications or other therapies,including electrical therapies, or both.

As used herein, a “movement state” may include a state in which thepatient is intending to move (e.g., initiating thoughts relating tomoving a body part, e.g., a limb or a leg to initiate movement), isattempting to initiate movement or has successfully initiated movementand is currently moving. A “sleep state” may include a state in whichthe patient is intending on sleeping (e.g., initiating thoughts ofsleep), is attempting to sleep or has initiated sleep and is currentlysleeping. A “speech state” may include a state in which the patient isintending on speaking, is attempting to speak or has initiated speech.

FIG. 1 is a conceptual diagram illustrating an example therapy system 10that is configured to deliver therapy to patient 12 to manage a disorderof patient 12. In some examples, therapy system 10 may deliver therapyto patient 12 to manage a movement disorder or a neurodegenerativeimpairment of patient 12. Patient 12 ordinarily will be a human patient.In some cases, however, therapy system 10 may be applied to othermammalian or non-mammalian non-human patients. A movement disorder maybe characterized by one or more symptoms, such as, but not limited to,impaired muscle control, motion impairment or other movement problems,such as rigidity, bradykinesia, rhythmic hyperkinesia, nonrhythmichyperkinesia, dystonia, tremor, and akinesia. In some cases, themovement disorder may be a symptom of Parkinson's disease orHuntington's disease. However, the movement disorder may be attributableto other patient conditions.

Although movement disorders are primarily referred to throughout theremainder of the application, in other examples, therapy system 10 maybe configured to deliver therapy to manage other patient conditions,such as, but not limited to, seizure disorders (e.g., epilepsy),psychiatric disorders, behavior disorders, mood disorders, memorydisorders, mentation disorders, Alzheimer's disease, or otherneurological or psychiatric impairments, in addition to or instead of amovement disorder. Examples of psychiatric disorders include majordepressive disorder (MDD), bipolar disorder, anxiety disorders, posttraumatic stress disorder, dysthymic disorder, and obsessive compulsivedisorder (OCD). Treatment of other patient disorders via delivery oftherapy to brain 28 or another suitable target therapy delivery site inpatient 12 is also contemplated.

In the example shown in FIG. 1, therapy system 10 includes medicaldevice programmer 14, implantable medical device (IMD) 16, leadextension 18, and one or more leads 20A and 20B (collectively “leads20”) with respective sets of electrodes 24, 26. IMD 16 includes atherapy module that includes a stimulation generator that is configuredto generate and deliver electrical stimulation therapy to one or moreregions of brain 28 of patient 12 via a subset of electrodes 24, 26 ofleads 20A and 20B, respectively. In the example shown in FIG. 1, therapysystem 10 may be referred to as a deep brain stimulation (DBS) systembecause IMD 16 provides electrical stimulation therapy directly totissue within brain 28, e.g., a tissue site under the dura mater ofbrain 28 or one or more branches or nodes, or a confluence of fibertracks. In other examples, leads 20 may be positioned to deliver therapyto a surface of brain 28 (e.g., the cortical surface of brain 28). Insome examples, IMD 16 may provide cortical stimulation therapy topatient 12, e.g., by delivering electrical stimulation to one or moretissue sites in the cortex of brain 28. In some examples, IMD 16 mayprovide vagal nerve stimulation (VNS) therapy to patient 12 bydelivering electrical stimulation to one or more vagal nerve tissuesites.

Although electrical stimulation therapy is primarily referred tothroughout the remainder of the application, in other examples, therapysystem 10 may be configured to deliver other types of therapy inaddition to or instead of electrical stimulation therapy, such as, e.g.,drug delivery therapy.

In the example shown in FIG. 1, IMD 16 may be implanted within asubcutaneous pocket in the pectoral region of patient 12. In otherexamples, IMP 16 may be implanted within other regions of patient 12,such as a subcutaneous pocket in the abdomen or buttocks of patient 12or proximate the cranium of patient 12. Implanted lead extension 18 iscoupled to IMD 16 via connector block 30 (also referred to as a header),which may include, for example, electrical contacts that electricallycouple to respective electrical contacts on lead extension 18. Theelectrical contacts electrically couple the electrodes 24, 26 carried byleads 20 to IMD 16. Lead extension 18 traverses from the implant site ofIMD 16 within a chest cavity of patient 12, along the neck of patient 12and through the cranium of patient 12 to access brain 28. IMD 16 can beconstructed of a biocompatible material that resists corrosion anddegradation from bodily fluids. IMD 16 may comprise a hermetic housing34 to substantially enclose components, such as a processor, therapymodule, and memory.

In the example shown in FIG. 1, leads 20 are implanted within the rightand left hemispheres, respectively, of brain 28 in order to deliverelectrical stimulation to one or more regions of brain 28, which may beselected based on many factors, such as the type of patient conditionfor which therapy system 10 is implemented to manage. Other implantsites for leads 20 and ID 16 are contemplated. For example, ID 16 may beimplanted on or within cranium 32 or leads 20 may be implanted withinthe same hemisphere at multiple target tissue sites or IMD 16 may becoupled to a single lead that is implanted in one or both hemispheres ofbrain 28.

Leads 20 may be positioned to deliver electrical stimulation to one ormore target tissue sites within brain 28 to manage patient symptomsassociated with a disorder of patient 12. Leads 20 may be implanted toposition electrodes 24, 26 at desired locations of brain 28 throughrespective holes in cranium 32. Leads 20 may be placed at any locationwithin brain 28 such that electrodes 24, 26 are capable of providingelectrical stimulation to target tissue sites within brain 28 duringtreatment. Different neurological or psychiatric disorders may beassociated with activity in one or more of regions of brain 28, whichmay differ between patients. For example, a suitable target therapydelivery site within brain 28 for controlling a movement disorder ofpatient 12 may include one or more of the pedunculopontine nucleus(PPN), thalamus, basal ganglia structures (e.g., globus pallidus,substantia nigra or subthalamic nucleus), zona inserta, fiber tracts,lenticular fasciculus (and branches thereof), ansa lenticularis, and/orthe Field of Forel (thalamic fasciculus). The PPN may also be referredto as the pedunculopontine tegmental nucleus.

As another example, in the case of MDD, bipolar disorder, OCD, or otheranxiety disorders, leads 20 may be implanted to deliver electricalstimulation to the anterior limb of the internal capsule of brain 28,and only the ventral portion of the anterior limb of the internalcapsule (also referred to as a VC/VS), the subgenual component of thecingulate cortex (which may be referred to as CG25), anterior cingulatecortex Brodmann areas 32 and 24, various parts of the prefrontal cortex,including the dorsal lateral and medial pre-frontal cortex (PFC) (e.g.,Brodmann area 9), ventromedial prefrontal cortex (e.g., Brodmann area10), the lateral and medial orbitofrontal cortex (e.g., Brodmann area11), the medial or nucleus accumbens, thalamus, intralaminar thalamicnuclei, amygdala, hippocampus, the lateral hypothalamus, the Locusceruleus, the dorsal raphe nucleus, ventral tegmentum, the substantianigra, subthalamic nucleus, the inferior thalamic peduncle, the dorsalmedial nucleus of the thalamus, the habenula, the bed nucleus of thestria terminalis, or any combination thereof. Target tissue sites notlocated in brain 28 of patient 12 are also contemplated.

As another example, in the case of a seizure disorder or Alzheimer'sdisease, for example, leads 20 may be implanted to deliver electricalstimulation to regions within the Circuit of Papez, such as, e.g., theanterior thalamic nucleus, the internal capsule, the cingulate, thefornix, the mammillary bodies, the mammillothalamic tract(mammillothalamic fasciculus), and/or hippocampus. For example, in thecase of a seizure disorder, IMD 16 may deliver therapy to a region ofbrain 28 via a selected subset of electrodes 24, 26 to suppress corticalactivity within the anterior thalamic nucleus, hippocampus, or otherbrain region associated with the occurrence of seizures (e.g., a seizurefocus of brain 28). Conversely, in the case of Alzheimer's disease, IMD16 may deliver therapy to a region of brain 28 via electrodes 24, 26 toincrease cortical activity within the anterior thalamic nucleus,hippocampus, or other brain region associated with Alzheimer's disease.As another example, in the case of depression (e.g., MDD), IMD 16 maydeliver therapy to a region of brain 28 via electrodes 24, 26 toincrease cortical activity within one or more regions of brain 28 toeffectively treat the patient disorder. As another example, ID 16 maydeliver therapy to a region of brain 28 via electrodes 24, 26 todecrease cortical activity within one or more regions of brain 28, suchas, e.g., the frontal cortex, to treat the disorder.

Although leads 20 are shown in FIG. 1 as being coupled to a common leadextension 18, in other examples, leads 20 may be coupled to IMD 16 viaseparate lead extensions or directly coupled to IMD 16. Moreover,although FIG. 1 illustrates system 10 as including two leads 20A and 20Bcoupled to IMD 16 via lead extension 18, in some examples, system 10 mayinclude one lead or more than two leads.

Leads 20 may be implanted within a desired location of brain 28 via anysuitable technique, such as through respective burr holes in the skullof patient 12 or through a common burr hole in the cranium 32. Leads 20may be placed at any location within brain 28 such that electrodes 24,26 of leads 20 are capable of providing electrical stimulation totargeted tissue during treatment. Electrical stimulation generated fromthe stimulation generator (not shown) within the therapy module of IMD16 may help mitigate the symptoms of movement disorders, such as byimproving the performance of motor tasks by patient 12 that mayotherwise be difficult. These tasks may include, for example, at leastone of initiating movement, maintaining movement, grasping and movingobjects, improving gait and balance associated with narrow turns, andthe like. The exact therapy parameter values of the electricalstimulation therapy that may help mitigate symptoms of the movementdisorder (or other patient condition) may be specific for the particulartarget stimulation site (e.g., the region of the brain) involved as wellas the particular patient and patient condition.

In the examples shown in FIG. 1, electrodes 24, 26 of leads 20 are shownas ring electrodes. Ring electrodes may be relatively easy to programand are typically capable of delivering an electrical field to anytissue adjacent to leads 20. In other examples, electrodes 24, 26 ofleads 20 may have different configurations. For example, electrodes 24,26 of leads 20 may have a complex electrode array geometry that iscapable of producing shaped electrical fields, including interleavedstimulation. An example of a complex electrode array geometry, mayinclude an array of electrodes positioned at different axial positionsalong the length of a lead, as well as at different angular positionsabout the periphery, e.g., circumference, of the lead. The complexelectrode array geometry may include multiple electrodes (e.g., partialring or segmented electrodes) around the perimeter of each lead 20, inaddition to, or instead of, a ring electrode. In this manner, electricalstimulation may be directed to a specific direction from leads 20 toenhance therapy efficacy and reduce possible adverse side effects fromstimulating a large volume of tissue. In some examples in which multipleleads 20 are implanted on the same hemisphere surrounding a target,steered electrical stimulation can be performed in between two or moreelectrodes.

In some examples, outer housing 34 of IMD 16 may include one or morestimulation and/or sensing electrodes. For example, housing 34 cancomprise an electrically conductive material that is exposed to tissueof patient 12 when IMD 16 is implanted in patient 12, or an electrodecan be attached to housing 34. In other examples, leads 20 may haveshapes other than elongated cylinders as shown in FIG. 1 with active orpassive tip configurations. For example, leads 20 may be paddle leads,spherical leads, bendable leads, or any other type of shape effective intreating patient 12.

IMD 16 may deliver electrical stimulation therapy to brain 28 of patient12 according to one or more stimulation therapy programs. A stimulationtherapy program may define one or more electrical stimulation parametervalues for therapy generated by a therapy module of IMD 16 and deliveredfrom IMD 16 to brain 28 of patient 12. Where IMD 16 delivers electricalstimulation in the form of electrical pulses, for example, theelectrical stimulation parameters may include amplitude mode (constantcurrent or constant voltage with or without multiple independent paths),pulse amplitude, pulse rate, pulse width, a waveform shape, and cyclingparameters (e.g., without cycling, duration of cycling, and the like).In addition, if different electrodes are available for delivery ofstimulation, a therapy parameter of a therapy program may be furthercharacterized by an electrode combination, which may define selectedelectrodes and their respective polarities.

In some examples, IMD 16 is configured to deliver electrical stimulationtherapy to brain 28 of patient 12 in an open loop manner, in which IMD16 delivers the stimulation therapy without intervention from a user ora sensor. In other examples, IMD 16 is configured to deliver electricalstimulation therapy to brain 28 of patient 12 in a closed loop manner ora pseudo-closed loop manner, in which IMD 16 controls the timing of thedelivery of electrical stimulation to brain 28, the output parameters ofthe electrical stimulation, or both based on one or more of user inputand input from a sensor. The sensor may, for example, provide feedbackthat may be used to control the electrical stimulation output from IMD16.

In addition to being configured to deliver therapy to manage a disorderof patient 12, therapy system 10 is configured to sense bioelectricalbrain signals of patient 12. For example, IMD 16 may include a sensingmodule that is configured to sense bioelectrical brain signals withinone or more regions of brain 28 via a subset of electrodes 24, 26,another set of electrodes, or both. Accordingly, in some examples,electrodes 24, 26 may be used to deliver electrical stimulation from thetherapy module to target sites within brain 28 as well as sense brainsignals within brain 28. However, IMD 16 can also use a separate set ofsensing electrodes to sense the bioelectrical brain signals. In theexample shown in FIG. 1, the signals generated by electrodes 24, 26 areconducted to the sensing module within IMD 16 via conductors within therespective lead 20A, 20B. In some examples, the sensing module of IMD 16may sense bioelectrical brain signals via one or more of the electrodes24, 26 that are also used to deliver electrical stimulation to brain 28.In other examples, one or more of electrodes 24, 26 may be used to sensebioelectrical brain signals while one or more different electrodes 24,26 may be used to deliver electrical stimulation.

Depending on the particular stimulation electrodes and sense electrodesused by IMD 16, IMD 16 may monitor bioelectrical brain signals anddeliver electrical stimulation at the same region of brain 28 or atdifferent regions of brain 28. In some examples, the electrodes used tosense bioelectrical brain signals may be located on the same lead usedto deliver electrical stimulation, while in other examples, theelectrodes used to sense bioelectrical brain signals may be located on adifferent lead than the electrodes used to deliver electricalstimulation. In some examples, a bioelectrical brain signal of patient12 may be monitored with external electrodes, e.g., scalp electrodes.Moreover, in some examples, the sensing module that senses bioelectricalbrain signals of brain 28 (e.g., the sensing module that generates anelectrical signal indicative of the activity within brain 28) is in aphysically separate housing from outer housing 34 of IMD 16. However, inthe example shown in FIG. 1 and the example primarily referred to hereinfor ease of description, the sensing module and therapy module of IMD 16are enclosed within a common outer housing 34.

The bioelectrical brain signals sensed by IMD 16 may reflect changes inelectrical current produced by the sum of electrical potentialdifferences across brain tissue. Example bioelectrical brain signalsinclude, but are not limited to, an electroencephalogram (EEG) signal,an electrocorticogram (ECoG) signal, a LFP sensed from within one ormore regions of a patient's brain, and/or action potentials from singlecells within the patient's brain. In some examples, LFP data can bemeasured ipsilaterally or contralaterally and considered as an average(e.g., a maximum or minimum or a heuristic combination thereof) or assome other value. The location at which the signals are obtained may beadjusted to a disease onset side of the body of patient 12 or severityof symptoms or disease duration. The adjustments, may, for example, bemade on the basis of clinical symptoms presented and their severity,which can be augmented or annotated with recorded LFP data. A clinicianor a processor of IMD 16 may also add heuristic weights to ipsilaterallyand/or contralaterally measured LFP data to be considered for systemfeedback.

Sensed bioelectrical brain signals of patient 12 may be used todetermine the patient state of patient 12. The patient state can be, forexample, a patient disease state, a state in which a symptom of apatient condition is observed, or a patient state indicative of theefficacy of therapy delivered by a medical device or the efficacy ofmedication. As discussed in further detail below, e.g., with respect toFIG. 4, in some examples, a processor of programmer 14, IMD 16, oranother device, alone or in combination with each other, determines apatient state based on activity of a bioelectrical brain signal of thepatient in one or more frequency sub-bands of a frequency band ofinterest. For example, the processor may determine a patient state basedon the power level in one or more particular more frequency sub-bands ofa frequency band of interest of a bioelectrical brain signal sensed byIMD 16, based on the spectral patterns of the bioelectrical brain signal(e.g., a shift in in peak power level from one frequency sub-band toanother over time), or any combination thereof. The processor maydetermine the patient state by, for example, receiving a sensedbioelectrical brain signal and detecting a predetermined biomarkerindicative of a particular patient state, the biomarker including one ormore spectral characteristics associated with the particular patientstate.

In some examples, the processor generates an indication of thedetermined patient state, controls therapy delivery to the patient basedon the determined patient state, monitors a patient condition based onthe determined patient state, generates a patient diagnosis (e.g.,determines a patient condition sub-type) based on the determined patientstate, or any combination thereof. For example, the processor cancontrol therapy delivery by, for example, modifying one or more therapyparameter values based on the determined patient state. One or moretherapy parameter values may be controlled in order to increase ordecrease the intensity of therapy delivery (e.g., by increasing ordecreasing one or more of the frequency, amplitude, or other stimulationparameter values), to initiate delivery of electrical stimulation, byIMD 16, to a target therapy delivery site in patient 12, or, dependingon the type of therapy delivery, to terminate delivery of electricalstimulation to the target therapy delivery site.

The processor may modify the therapy delivered by IMD 16 using anysuitable technique. In some examples, the processor modifies therapy byat least modifying at least one therapy parameter value with which IMD16 generates and delivers therapy to patient 12. The at least onetherapy parameter value may be a part of a therapy program that definesvalues for a plurality of therapy parameters. As a result, in someexamples, the processor may modify at least one therapy parameter valueby at least modifying a therapy program (e.g., changing the value of atleast one therapy parameter of the therapy program or selecting a newtherapy program).

In some examples, IMD 16 may be configured to sense the bioelectricalbrain signal (e.g., by measuring a LFP) at periodic, predetermined(which may also be periodic), or random intervals, or in response to apatient input or another trigger. In other examples, IMD 16 continuouslysenses the bioelectrical brain signal, but the processor only samplesthe sensed bioelectrical brain signal (e.g., the last storedbioelectrical brain signal) and determines whether the sample includesthe biomarker at predetermined periodic times or in response to userinput (e.g., input/trigger from a patient).

External programmer 14 is configured to wirelessly communicate with IMD16 as needed to provide or retrieve therapy information. Programmer 14is an external computing device that the user, e.g., the clinicianand/or patient 12, may use to communicate with IMD 16. For example,programmer 14 may be a clinician programmer that the clinician uses tocommunicate with IMD 16 and program one or more therapy programs for ID16. In addition, or instead, programmer 14 may be a patient programmerthat allows patient 12 to select programs and/or view and modify therapyparameter values. The clinician programmer may include more programmingfeatures than the patient programmer. In other words, more complex orsensitive tasks may only be allowed by the clinician programmer toprevent an untrained patient from making undesired changes to IMD 16.

Programmer 14 may be a hand-held computing device with a displayviewable by the user and an interface for providing input to programmer14 (i.e., a user input mechanism). For example, programmer 14 mayinclude a small display screen (e.g., a liquid crystal display (LCD) ora light emitting diode (LED) display) that presents information to theuser. In addition, programmer 14 may include a touch screen display,keypad, buttons, a peripheral pointing device or another input mechanismthat allows the user to navigate though the user interface of programmer14 and provide input. If programmer 14 includes buttons and a keypad,the buttons may be dedicated to performing a certain function, i.e., apower button, the buttons and the keypad may be soft keys that change infunction depending upon the section of the user interface currentlyviewed by the user, or any combination thereof. Alternatively, thescreen (not shown) of programmer 14 may be a touch screen that allowsthe user to provide input directly to the user interface shown on thedisplay. The user may use a stylus or their finger to provide input tothe display.

In other examples, programmer 14 may be a larger workstation or aseparate application within another multi-function device, rather than adedicated computing device. For example, the multi-function device maybe a notebook computer, tablet computer, workstation, cellular phone,personal digital assistant or another computing device that may run anapplication that enables the computing device to operate as a securemedical device programmer 14. A wireless adapter coupled to thecomputing device may enable secure communication between the computingdevice and IMD 16.

When programmer 14 is configured for use by the clinician, programmer 14may be used to transmit initial programming information to IMD 16. Thisinitial information may include hardware information, such as the typeof leads 20, the arrangement of electrodes 24, 26 on leads 20, theposition of leads 20 within brain 28, initial programs defining therapyparameter values, and any other information that may be useful forprogramming into IMD 16. Programmer 14 may also be capable of completingfunctional tests (e.g., measuring the impedance of electrodes 24, 26 ofleads 20).

The clinician may also generate and store therapy programs within IMD 16with the aid of programmer 14. During a programming session, theclinician may determine one or more therapy programs that may provideefficacious therapy to patient 12 to address symptoms associated withthe movement disorder (or other patient conditions). For example, theclinician may select one or more electrode combinations with whichstimulation is delivered to brain 28. During the programming session,patient 12 may provide feedback to the clinician as to the efficacy ofthe specific program being evaluated or the clinician may evaluate theefficacy based on one or more sensed or observable physiologicalparameters of patient (e.g., muscle activity) or based on motiondetected via one or more motion sensors that generate signals indicativeof motion of patient 12. Programmer 14 may assist the clinician in thecreation/identification of therapy programs by providing a methodicalsystem for identifying potentially beneficial therapy parameter values.

Programmer 14 may also be configured for use by patient 12. Whenconfigured as a patient programmer, programmer 14 may have limitedfunctionality (compared to a clinician programmer) in order to preventpatient 12 from altering critical functions of IMD 16 or applicationsthat may be detrimental to patient 12.

Whether programmer 14 is configured for clinician or patient use,programmer 14 is configured to communicate to IMD 16 and, optionally,another computing device, via wireless communication. Programmer 14, forexample, may communicate via wireless communication with IMD 16 usingradio frequency (RF) telemetry techniques known in the art. Programmer14 may also communicate with another programmer or computing device viaa wired or wireless connection using any of a variety of local wirelesscommunication techniques, such as RF communication according to the802.11 or Bluetooth specification sets, infrared (IR) communicationaccording to the IRDA specification set, or other standard orproprietary telemetry protocols. Programmer 14 may also communicate withother programming or computing devices via exchange of removable media,such as magnetic or optical disks, memory cards or memory sticks.Further, programmer 14 may communicate with IMD 16 and anotherprogrammer via remote telemetry techniques known in the art,communicating via a local area network (LAN), wide area network (WAN),public switched telephone network (PSTN), or cellular telephone network,for example.

Therapy system 10 may be implemented to provide chronic stimulationtherapy to patient 12 over the course of several months or years.However, system 10 may also be employed on a trial basis to evaluatetherapy before committing to full implantation. If implementedtemporarily, some components of system 10 may not be implanted withinpatient 12. For example, patient 12 may be fitted with an externalmedical device, such as a trial stimulator, rather than IMD 16. Theexternal medical device may be coupled to percutaneous leads or toimplanted leads via a percutaneous extension. If the trial stimulatorindicates DBS system 10 provides effective treatment to patient 12, theclinician may implant a chronic stimulator within patient 12 forrelatively long-term treatment.

System 10 shown in FIG. 1 is merely one example of a therapy system thatis configured to determine a patient state based on activity of abioelectrical brain signal of patient 12 in one or more frequencysub-bands of a frequency band. Systems with other configurations ofleads, electrodes, and sensors are possible. For example, in otherimplementations, IMD 16 may be coupled to additional leads or leadsegments having one or more electrodes positioned at different targettissue sites, which may be within brain 28 or outside of brain (e.g.,proximate to a spinal cord of patient 12, a peripheral nerve of patient12, a muscle of patient 12, or any other suitable therapy deliverysite). The additional leads may be used for delivering differentstimulation therapies to respective stimulation sites within patient 12or for monitoring at least one physiological parameter of patient 12.

Additionally, in other examples, a system may include more than one IMD.For example, a system may include two IMDs coupled to respective one ormore leads. Each IMD can deliver stimulation to a respective lateralside of patient 12 in some examples.

As another example configuration, a therapy system can include one ormore leadless electrical stimulators (e.g., microstimulators having asmaller form factor than IMD 16 and may not be coupled to any separateleads). The leadless electrical stimulators can be configured togenerate and deliver electrical stimulation therapy to patient 12 viaone or more electrodes on an outer housing of the electrical stimulator.In examples including a plurality of leadless electrical stimulators,the leadless electrical stimulators can be implanted at different targettissue sites within patient 12. One electrical stimulator may act as a“master” module that coordinates the delivery of stimulation to patient12 via the plurality of electrical stimulators.

In some examples, IMD 16 is not configured to delivery electricalstimulation therapy to brain of patient 12, but, rather, is onlyconfigured to sense one or more physiological parameters of patient 12,including a bioelectrical brain signal of patient 12. This type of IMD16 may a patient monitoring device useful for diagnosing patient 12,monitoring a patient condition 12, or to train IMD 16 or another ID fortherapy delivery. For example, during a learning phase, a processor ofIMD 16 or another processor can determine, based on the oscillationspectra of a bioelectrical brain signal sensed by IMD 16, spectralcharacteristics of a bioelectrical brain signal indicative of one ormore specific patient states (e.g., normal brain function, abnormalbrain function, specific patient symptoms, patient, a movement state, asleep state, a speech state, and the like). Example spectralcharacteristics include, for example, power levels in one or morefrequency sub-bands, spectral patterns (e.g., a pattern in the activityin one or more frequency sub-bands over time), or any combinationthereof. These spectral characteristics may be, for example, stored byprogrammer 14, IMD 16, or another device as biomarkers indicative of aparticular patient state, such as normal or pathological behaviors,responses to medications or other therapies, including electricaltherapies, or both.

FIG. 2 is functional block diagram illustrating components of an exampleIMD 16. In the example shown in FIG. 2, IMD 16 includes processor 60,memory 62, stimulation generator 64, sensing module 66, switch module68, telemetry module 70, and power source 72. Memory 62, as well asother memories described herein, may include any volatile ornon-volatile media, such as a random access memory (RAM), read onlymemory (ROM), non-volatile RAM (NVRAM), electrically erasableprogrammable ROM (EEPROM), flash memory, and the like. Memory 62 maystore computer-readable instructions that, when executed by processor60, cause ID 16 to perform various functions described herein.

In the example shown in FIG. 2, memory 62 stores therapy programs 74,biomarker information 76, and operating instructions 78, e.g., inseparate memories within memory 62 or separate areas within memory 62.Each stored therapy program 74 defines a particular program of therapyin terms of respective values for electrical stimulation parameters,such as a stimulation electrode combination, electrode polarity, currentor voltage amplitude, and, if stimulation generator 64 generates anddelivers stimulation pulses, the therapy programs may define values fora pulse width, and pulse rate of a stimulation signal. Each storedtherapy program 74 may also be referred to as a set of therapy parametervalues. In some examples, the therapy programs may be stored as atherapy group, which defines a set of therapy programs with whichstimulation may be generated. The stimulation signals defined by thetherapy programs of the therapy group may be delivered together on anoverlapping or non-overlapping (e.g., time-interleaved) basis.

Biomarker information 76 stored by memory 62 includes one or morespectral characteristics indicative of a particular patient state. Insome examples, each biomarker is associated with a patient state. Eachpatient state of a plurality of patient states can be associated withone or more respective biomarkers. In other examples, each patient stateof a plurality of patient states is associated with one respectivebiomarker. Stored biomarkers include biomarkers that indicate activityof a bioelectrical brain signal of a patient in one or more frequencysub-bands of a frequency band of interest. For example, biomarkerinformation 76 may store one or more biomarkers that indicate a spectralpattern of a bioelectrical brain signal in a frequency band of interest,such as a pattern in the power distribution between sub-bands of thefrequency band over time. An example pattern in the power distributionthat may be stored as a biomarker includes a shift in a powerdistribution (e.g., a peak power or highest relative average power)between sub-bands of the frequency band over time, such as a shift ofthe peak power from a frequency of about 15 Hz to a frequency of about20 Hz, where 15 Hz and 20 Hz are in different frequency sub-bands, or ashift from an average peak power for a frequency sub-band from a firstfrequency sub-band (e.g., about 19 Hz to about 21 Hz) to a secondfrequency sub-band (e.g., about 13 Hz to about 18 Hz). Other examplespattern in the power distribution that may be stored as a biomarkerincludes the frequency sub-band in which a peak power within a frequencyband of interest is observed, or the frequency sub-band in which a peakpower of the overall peak power level of the bioelectrical brain signalis observed. The frequency sub-bands may be predefined prior to anyautomatic patient state detection by IMD 16.

In addition, or instead, a spectral pattern of a bioelectrical brainsignal stored by biomarker information 76 as a biomarker for aparticular patient state may include a change in the sub-band having thepeak frequency or dominant activity, such as a shift from one particularfrequency sub-band to another. A spectral pattern of a bioelectricalbrain signal stored by biomarker information 76 as a biomarker for aparticular patient state may include a pattern of the power distributionover one or more frequency sub-bands (e.g., a narrow peak, a broad peak,a unimodal peak, or a bimodal peak), or a width or a variability (e.g.,in the width) of one or more frequency sub-bands exhibiting a relativelyhigh or low level of activity, e.g., relative to a predeterminedthreshold of such activity. Any combination of the spectral patterns maybe stored as biomarker information 76.

The frequency sub-band exhibiting the relatively high activity can be,for example, the frequency sub-band having the dominant activity in thefrequency band of interest or having an activity level greater than orequal to a threshold value. The frequency sub-band exhibiting therelatively low activity can be, for example, the frequency sub-bandhaving the lowest amount of activity in the frequency band of interestor having an activity level less than or equal to a threshold value. Itis believed that in some cases, a frequency sub-band exhibiting therelatively high activity may be indicative of a sub-type of the patientcondition, such as a sub-type of Parkinson's disease. For example, adominant amount of beta band activity in a relatively low frequencysub-band of the beta band may indicate a different type of Parkinson'sdisease than a dominant amount of beta band activity in a relativelyhigh frequency sub-band of the beta band.

It is believed that in some cases, a characteristic of a distribution ofa signal strength (also referred to herein as a “power distribution”)may be indicative of a sub-type of the patient condition, such as asub-type of Parkinson's disease, or another patient state. The signalstrength distribution can be determined, e.g., by a processor of adevice, based on a plot of a power level versus frequency of thefrequency domain characteristics of the bioelectrical brain signal. Insome examples, a predetermined characteristic of a distribution of asignal strength within the frequency band of the bioelectrical brainsignal indicative of a patient state can include a shape of the peak ofthe plot. For example, a relatively sharp peak (e.g., a peak having awidth less than, or less than or equal to, a threshold value) may beassociated with a different type of Parkinson's disease than arelatively broad peak (e.g., a peak having a greater less than, orgreater than or equal to, a threshold value). The width of a peak of thefrequency domain plot of the bioelectrical brain signal can be measuredin units of Hertz (e.g., indicating the frequency span of the peak), andcan be determined to be the width (as indicated by the frequency span)from one point at a particular percentage of the peak power (e.g., about90% of the peak power) to another point at the particular percentage ofthe peak power. For example, the width of a peak can be the width from apoint that is 90% of the peak power to the next point that is 90% of thepeak power (and at a different frequency).

As another example, a unimodal peak may be associated with a differenttype of Parkinson's disease than a bimodal peak. Thus, biomarkerinformation 76 may store one or more biomarkers indicative of andassociated with these different sub-types of a patient condition.Detection of a particular biomarker may, therefore, but useful fordiagnosing a patient.

Processor 60 (or processor of another device, such as programmer 14) candetermine a biomarker that indicates a power distribution of abioelectrical brain signal based on a waveform generated from a plot ofthe power level versus frequency. The biomarker can be, for example, anyparameters that indicate a specific pattern in the power distribution.For example, the biomarker can be a template signal stored by memory 62.Processor 60 can detect such a biomarker in a sensed bioelectrical brainsignal by, for example, correlating the sensed bioelectrical brainsignal with the template signal and detecting the biomarker in responseto determining there is a substantial correlation (e.g., about 80%,about 90%, or about 95% or more) between a sensed bioelectrical brainsignal and the template signal.

As another example, the biomarker can be a difference in the powerdistribution relative to a baseline bioelectrical brain signal. In someexamples, the baseline bioelectrical brain signal can be, for example, asignal that indicates a patient state in which no therapeutic effects ofthe therapy are observed (e.g., a pathological state) or a state inwhich no patient symptoms are observed. In either case, the baselinebioelectrical brain signal may be patient-specific or may be moregeneral to a plurality of patients.

As another example, biomarker information 76 may store one or morebiomarkers that indicate the power level of a bioelectrical brain signalof the patient in one or more frequency sub-bands of a frequency band ora threshold power level associated with a patient state. For example, apower level greater than or equal to a threshold value in a particularfrequency sub-band may be associated with a particular patient state. Asanother example, a power level less than or equal to a threshold valuein a particular frequency sub-band may be associated with a particularpatient state.

In any of these examples, the particular characteristics of a biomarkermay vary between patients, as well as may vary depending on the area ofbrain 28 in which the electrodes 24, 26 used to sense the bioelectricalbrain signal are implanted, which may depend on the depth of theelectrodes 24, 26 in brain 28.

In some examples, memory 62 may also store brain signal data generatedby sensing module 66 via at least one of electrodes 24, 26 and, in somecases, at least a portion of outer housing 34 of IMD 16, an electrode onouter housing 34 of IMD 16 or another reference. In addition, in someexamples, processor 60 may append a time and date stamp to the brainsignal data in memory 62. Operating instructions 78 guide generaloperation of IMD 16 under control of processor 60, and may includeinstructions for monitoring brains signals within one or more brainregions via electrodes 24, 26 and delivering electrical stimulationtherapy to patient 12.

Stimulation generator 64, under the control of processor 60, generatesstimulation signals for delivery to patient 12 via selected combinationsof electrodes 24, 26. In some examples, stimulation generator 64generates and delivers stimulation signals to one or more target regionsof brain 28 (FIG. 1), via a select combination of electrodes 24, 26,based on one or more stored therapy programs 74. The target tissue siteswithin brain 28 for stimulation signals or other types of therapy andstimulation parameter values may depend on the patient condition forwhich therapy system 10 is implemented to manage. While stimulationpulses are described, stimulation signals may be of any form, such ascontinuous-time signals (e.g., sine waves) or the like.

The processors described in this disclosure, including processor 60, mayinclude one or more digital signal processors (DSPs), general purposemicroprocessors, application specific integrated circuits (ASICs), fieldprogrammable logic arrays (FPGAs), or other equivalent integrated ordiscrete logic circuitry, or combinations thereof. The functionsattributed to processors described herein may be provided by a hardwaredevice and embodied as software, firmware, hardware, or any combinationthereof. Processor 60 is configured to control stimulation generator 64according to therapy programs 74 stored by memory 62 to apply particularstimulation parameter values specified by one or more programs, such asamplitude, pulse width, and pulse rate.

In the example shown in FIG. 2, the set of electrodes 24 of lead 20Aincludes electrodes 24A, 24B, 24C, and 24D, and the set of electrodes 26of lead 20B includes electrodes 26A, 26B, 26C, and 26D. Processor 60 maycontrol switch module 68 to apply the stimulation signals generated bystimulation generator 64 to selected combinations of electrodes 24, 26.In particular, switch module 68 may couple stimulation signals toselected conductors within leads 20, which, in turn, deliver thestimulation signals across selected electrodes 24, 26. Switch module 68may be a switch array, switch matrix, multiplexer, or any other type ofswitching module configured to selectively couple stimulation energy toselected electrodes 24, 26 and to selectively sense bioelectrical brainsignals with selected electrodes 24, 26. Hence, stimulation generator 64is coupled to electrodes 24, 26 via switch module 68 and conductorswithin leads 20. In some examples, however, IMD 16 does not includeswitch module 68.

Stimulation generator 64 may be a single channel or multi-channelstimulation generator. In particular, stimulation generator 64 may becapable of delivering, a single stimulation pulse, multiple stimulationpulses or continuous signal at a given time via a single electrodecombination or multiple stimulation pulses at a given time via multipleelectrode combinations. In some examples, however, stimulation generator64 and switch module 68 may be configured to deliver multiple channelson a time-interleaved basis. For example, switch module 68 may serve totime divide the output of stimulation generator 64 across differentelectrode combinations at different times to deliver multiple programsor channels of stimulation energy to patient 12.

Sensing module 66, under the control of processor 60, is configured tosense bioelectrical brain signals of patient 12 via a selected subset ofelectrodes 24, 26 or with one or more electrodes 24, 26 and at least aportion of a conductive outer housing 34 of IMD 16, an electrode on anouter housing of IMD 16 or another reference. Processor 60 may controlswitch module 68 to electrically connect sensing module 66 to selectedelectrodes 24, 26. In this way, sensing module 66 may selectively sensebioelectrical brain signals with different combinations of electrodes24, 26 (and/or a reference other than an electrode 24, 26). Aspreviously described, processor 60 may monitor the efficacy of therapydelivery by IMD 16 via the sensed bioelectrical brain signals anddetermine whether the efficacy of therapy delivery has changed, and, inresponse, generate a notification (e.g., to patient 12 or patientcaretaker).

In some examples, sensing module 66 includes a frequency selectivesensing circuit that extracts the energy level within one or moreselected frequency bands and sub-bands of a sensed patient parametersignal, which may be, for example, a bioelectrical brain signal. Thefrequency selective sensing circuit can include a chopper-stabilizedsuperheterodyne instrumentation amplifier and a signal analysis unit,and may utilize a heterodyning, chopper-stabilized amplifierarchitecture to convert a selected frequency band (or frequencysub-band) of a physiological signal, such as a bioelectrical brainsignal, to a baseband for analysis. The physiological signal may beanalyzed in one or more selected frequency bands to determine one ormore features as described herein. In some examples, sensing module 66includes a plurality of channels that extract the same or differentfrequency bands (or sub-bands) of one or more signals indicative of oneor more patient parameters.

Examples of various additional chopper amplifier circuits that may besuitable for or adapted to the techniques, circuits and devices of thisdisclosure are described in U.S. Pat. No. 7,385,443 to Denison, which isentitled “CHOPPER STABILIZED INSTRUMENTATION AMPLIFIER” and issued onJan. 10, 2008, the entire content of which is incorporated herein byreference. Examples of frequency selective monitors that may utilize aheterodyning, chopper-stabilized amplifier architecture are described inU.S. Provisional Application No. 60/975,372 by Denison et al., entitled“FREQUENCY SELECTIVE MONITORING OF PHYSIOLOGICAL SIGNALS,” and filed onSep. 26, 2007, commonly-assigned U.S. Provisional Application No.61/025,503 by Denison et al., entitled “FREQUENCY SELECTIVE MONITORINGOF PHYSIOLOGICAL SIGNALS, and filed on Feb. 1, 2008, andcommonly-assigned U.S. Provisional Application No. 61/083,381, entitled,“FREQUENCY SELECTIVE EEG SENSING CIRCUITRY,” and filed on Jul. 24, 2008.The entire contents of above-identified U.S. Provisional ApplicationNos. 60/975,372, 61/025,503, and 61/083,381 are incorporated herein byreference. Further examples of chopper amplifier circuits are alsodescribed in further detail in commonly-assigned U.S. Patent ApplicationPublication No. 2009/0082691 by Denison et al., entitled, “FREQUENCYSELECTIVE MONITORING OF PHYSIOLOGICAL SIGNALS” and filed on Sep. 25,2008. U.S. Patent Application Publication No. 2009/0082691 by Denison etal. is incorporated herein by reference in its entirety.

A sensing module 66 that directly extracts energy in key frequency bandsof a bioelectrical brain signal may be used to extract bandpower at keyphysiological frequencies with an architecture that is flexible, robust,and relatively low-noise. Chopper stabilization is a noise and powerefficient architecture for amplifying low-frequency physiologicalsignals in micropower applications (e.g., an implanted device) withexcellent process immunity. Chopper stabilized amplifiers can be adaptedto provide wide dynamic range, high-Q filters. A sensing module 66 thatincludes a chopper-stabilized amplifier may slightly displace the clockswithin the chopper amplifier in order to re-center a targeted band ofenergy to direct current (DC) in a manner similar to superheterodynereceivers used in communication systems. In some examples, extractingthe bandpower within a selected frequency band requires two parallelsignal paths (in-phase and quadrature) that are combined within thepower extraction stage. The power output signal can be lowpass filtered,which results in an output that represents the spectral powerfluctuations in the frequency band.

As previously indicated, a bioelectrical brain signal may include anEEG, ECoG, single cell recording, or LFP. The band power fluctuations inLFPs sensed within brain 28 of patient 12 (FIG. 1) can be orders ofmagnitude slower than the frequency at which they are encoded, so theuse of efficient analog preprocessing before performing analog todigital conversion can greatly reduce the overall energy requirementsfor implementing a complete mixed-signal system. Thus, a sensing module66 that includes a circuit architecture that directly extracts energy inkey frequency bands (or sub-bands) of a bioelectrical brain signal maybe useful for tracking the relatively slow power fluctuations within theselected frequency bands and determining a patient state based on thebioelectrical brain signal.

Although sensing module 66 is incorporated into a common housing 34 withstimulation generator 64 and processor 60 in FIG. 2, in other examples,sensing module 66 is in a separate outer housing from outer housing 34of IMD 16 and communicates with processor 60 via wired or wirelesscommunication techniques.

In some examples, processor 60 (or another processor of system 10) maybe configured to modify therapy delivered by IMD 16 in response todetecting a biomarker in a bioelectrical brain signal sensed by sensingmodule 66. In this way, a bioelectrical brain signal sensed by sensingmodule 66 may be used for closed-loop control of electrical stimulationdelivery by IMD 16.

Telemetry module 70 is configured to support wireless communicationbetween IMD 16 and an external programmer 14 or another computing deviceunder the control of processor 60. Processor 60 of IMD 16 may receive,as updates to programs, values for various stimulation parameters suchas amplitude and electrode combination, from programmer 14 via telemetrymodule 70. The updates to the therapy programs may be stored withintherapy programs 74 portion of memory 62. Telemetry module 70 in IMD 16,as well as telemetry modules in other devices and systems describedherein, such as programmer 14, may accomplish communication by RFcommunication techniques. In addition, telemetry module 70 maycommunicate with external medical device programmer 14 via proximalinductive interaction of IMD 16 with programmer 14. Accordingly,telemetry module 70 may send information to external programmer 14 on acontinuous basis, at periodic intervals, or upon request from IMD 16 orprogrammer 14. For example, processor 60 may transmit brain stateinformation 76 to programmer 14 via telemetry module 70.

Power source 72 delivers operating power to various components of IMD16. Power source 72 may include a small rechargeable or non-rechargeablebattery and a power generation circuit to produce the operating power.Recharging may be accomplished through proximal inductive interactionbetween an external charger and an inductive charging coil within IMD16. In some examples, power requirements may be small enough to allowIMD 16 to utilize patient motion and implement a kineticenergy-scavenging device to trickle charge a rechargeable battery. Inother examples, traditional batteries may be used for a limited periodof time.

FIG. 3 is a functional block diagram illustrating components of anexample medical device programmer 14 (FIG. 1). Programmer 14 includesprocessor 80, memory 82, telemetry module 84, user interface 86, andpower source 88. Processor 80 controls user interface 86 and telemetrymodule 84, and stores and retrieves information and instructions to andfrom memory 82. Programmer 14 may be configured for use as a clinicianprogrammer or a patient programmer. Processor 80 may comprise anycombination of one or more processors including one or moremicroprocessors, DSPs, ASICs, FPGAs, or other equivalent integrated ordiscrete logic circuitry. Accordingly, processor 80 may include anysuitable structure, whether in hardware, software, firmware, or anycombination thereof, to perform the functions ascribed herein toprocessor 80.

A user, such as a clinician or patient 12, may interact with programmer14 through user interface 86. User interface 86 includes a display (notshown), such as a LCD or LED display or other type of screen, with whichprocessor 80 may present information related to the therapy, a patientcondition detected by programmer 14 or IMD 16 based on a frequencydomain characteristic of a sensed bioelectrical brain signal, orelectrical signals sensed via a plurality of sense electrodecombinations. In addition, user interface 86 may include an inputmechanism to receive input from the user. The input mechanisms mayinclude, for example, buttons, a keypad (e.g., an alphanumeric keypad),a peripheral pointing device or another input mechanism that allows theuser to navigate though user interfaces presented by processor 80 ofprogrammer 14 and provide input.

If programmer 14 includes buttons and a keypad, the buttons may bededicated to performing a certain function, i.e., a power button, or thebuttons and the keypad may be soft keys that change function dependingupon the section of the user interface currently viewed by the user. Inaddition, or instead, the screen (not shown) of programmer 14 may be atouch screen that allows the user to provide input directly to the userinterface shown on the display. The user may use a stylus or theirfinger to provide input to the display. In other examples, userinterface 86 also includes audio circuitry for providing audiblenotifications, instructions or other sounds to patient 12, receivingvoice commands from patient 12, which may be useful if patient 12 haslimited motor functions, or both. Patient 12, a clinician or anotheruser may also interact with programmer 14 to manually select therapyprograms, generate new therapy programs, modify therapy programs throughindividual or global adjustments, and transmit the new programs to IMD16.

In some examples, at least some of the control of therapy delivery byIMD 16 may be implemented by processor 80 of programmer 14. For example,in some examples, processor 80 may receive sensed brain signalinformation from IMD 16 or from a sensing module that is separate fromIMD 16. The separate sensing module may, but need not be, implantedwithin patient 12. Brain signal information may include, for example, araw bioelectrical brain signal, parameterized bioelectrical brainsignal, or any other suitable information indicative of a bioelectricalbrain signal sensed by sensing module 66. Processor 80 may determine apatient stated based on the received brain signal information, e.g.,using any of the techniques described herein. In addition, in someexamples, processor 80 may generate an indication of the determinedpatient state, control therapy delivery by IMD 16 to patient 12 based onthe determined patient state, monitor a patient condition based on thedetermined patient state, generate a patient diagnosis (e.g., determinesa patient condition sub-type) based on the determined patient state, orany combination thereof.

Memory 82 may include instructions for operating user interface 86 andtelemetry module 84, and for managing power source 88. In some examples,memory 82 may also store any therapy data retrieved from IMD 16 duringthe course of therapy, biomarker information, sensed bioelectrical brainsignals, and the like. In some instances, the clinician may use thistherapy data to determine the progression of the patient condition inorder to plan future treatment for the movement disorder (or otherpatient condition) of patient 12. Memory 82 may include any volatile ornonvolatile memory, such as RAM, ROM, EEPROM or flash memory. Memory 82may also include a removable memory portion that may be used to providememory updates or increases in memory capacities. A removable memory mayalso allow sensitive patient data to be removed before programmer 14 isused by a different patient.

Wireless telemetry in programmer 14 may be accomplished by RFcommunication or proximal inductive interaction of external programmer14 with IMD 16. This wireless communication is possible through the useof telemetry module 84. Accordingly, telemetry module 84 may be similarto the telemetry module contained within IMD 16. In other examples,programmer 14 may be capable of infrared communication or directcommunication through a wired connection. In this manner, other externaldevices may be capable of communicating with programmer 14 withoutneeding to establish a secure wireless connection.

Power source 88 is configured to deliver operating power to thecomponents of programmer 14. Power source 88 may include a battery and apower generation circuit to produce the operating power. In someexamples, the battery may be rechargeable to allow extended operation.Recharging may be accomplished by electrically coupling power source 88to a cradle or plug that is connected to an alternating current (AC)outlet. In addition, recharging may be accomplished through proximalinductive interaction between an external charger and an inductivecharging coil within programmer 14. In other examples, traditionalbatteries (e.g., nickel cadmium or lithium ion batteries) may be used.In addition, programmer 14 may be directly coupled to an alternatingcurrent outlet to operate.

FIG. 4 is a flow diagram illustrating an example technique fordetermining a patient state based on a sensed bioelectrical brain signaland taking a responsive action in response to the determined patientstate. While the technique shown in FIG. 4, as well as many othertechniques, are described with respect to processor 60 of IMD 16, inother examples, a processor of another device, such as processor 80 ofprogrammer 14 (FIG. 3), can perform any part of the techniques describedherein, alone or in combination with another device.

In accordance with the technique shown in FIG. 4, processor 60 of IMD 16receives a bioelectrical brain signal sensed by sensing module 66 (100).For example, processor 60 may control sensing module 66 to sense abioelectrical brain signal of patient 12, e.g., via one or more ofelectrodes 24, 26 on leads 20, and sensing module 66 may transmit thesensed bioelectrical brain signal to processor 60. Processor 60 mayreceive the bioelectrical brain signal at any suitable time. In someexamples, processor 60 receives the bioelectrical brain signal sensed bysensing module 66 at randomly or pseudo-randomly selected times or atpredetermined intervals, while in other examples, processor 60 receivesthe bioelectrical brain signal sensed by sensing module 66 substantiallycontinuously. The frequency with which processor 60 receives thebioelectrical brain signal sensed by sensing module 66 may be selectedby a clinician in some examples.

While some portions of the disclosure generally refer to processor 60(or another processor) receiving a bioelectrical brain signal, this mayindicate that processor 60 (or another processor) receives informationrepresentative of the bioelectrical brain signal. The informationrepresentative of the bioelectrical brain signal may be, for example, araw bioelectrical brain signal sensed by sensing module 66 of IMD 16 (oranother sensing module), a parameterized bioelectrical brain signalgenerated by sensing module 66 or data generated based on the rawbioelectrical brain signal, such as one or more signal characteristicsextracted from the sensed bioelectrical brain signal.

In addition or instead of automatically receiving sensed bioelectricalbrain signals from sensor 66, in some examples, processor 60 isconfigured to receive the bioelectrical brain signal sensed by sensingmodule 66 in response to user input initiating a bioelectrical brainsignal sensing. Processor 60 may receive the user input, for example,via IMD 16 or via programmer 14.

In the technique shown in FIG. 4, processor 60 of IMD 16 determineswhether the sensed bioelectrical brain signal includes a biomarker(102). Example biomarkers are described above with respect to FIG. 2. Asdiscussed above, the biomarker can indicate specific activity of abioelectrical brain signal of patient 12 in one or more frequencysub-bands of a frequency band of interest and associated with a patientstate, such as a power level in one or more frequency sub-bands or aspectral pattern of a bioelectrical brain signal in a frequency band ofinterest.

Processor 60 may employ one or more suitable signal processingtechniques to determine whether a sensed bioelectrical brain signal hasa biomarker indicative of a change in efficacy of electrical stimulationtherapy delivered by IMD 16. In some examples, in order to determinewhether a sensed bioelectrical brain signal includes the biomarker,processor 60 determine one or more frequency band (spectral)characteristics of a sensed bioelectrical brain signal and determine thesensed bioelectrical brain signal includes the biomarker in response todetermining the one or more frequency band characteristics meet aparticular set of criteria (e.g., a particular spectral pattern)associated with the biomarker.

In some examples, processor 60 substantially continuously receives(e.g., continuously receives or nearly continuously receives) thebioelectrical brain signal sensed by sensing module 66, but only samplesthe bioelectrical brain signal and determines whether the sampledbioelectrical brain signal includes a biomarker (102) at predeterminedintervals, random (or pseudo-random) intervals, in response to userinput, or any combination thereof (e.g., as described above with respectto receiving the bioelectrical brain signal). The frequency with whichprocessor 60 samples the sensed bioelectrical brain signal or determineswhether the sensed bioelectrical brain signal includes a biomarker maybe selected by a clinician in some examples.

In response to determining the sensed bioelectrical brain signalincludes a biomarker, processor 60 may detect a patient state and take aresponsive action (104). In some examples, the responsive actionincludes generating an indication of the determined patient state.Processor 60 may store the indication in memory 62 of IMD 16, memory 82of programmer 14, or a memory of another device. In some examples,processor 60 generates the indication by at least transmitting anotification to a user, such as patient 12 or a patient caretaker, e.g.,via user interface 86 (FIG. 3) of programmer 14.

Processor 60 may be configured to provide a notification using anysuitable technique. In some examples, processor 60 may be configured tocontrol programmer 14 to display a visible message, emit an audiblealert signal or provide a somatosensory alert (e.g., by causing ahousing of programmer 14 to vibrate in a particular pattern or tovibrate continuously for a period of time) via user interface 86 inorder to provide the notification, or any combination of theaforementioned types of notifications. In addition to or instead of thenotifications provided via programmer 14, the notifications may beprovided via another external device or via IMD 16. For example,processor 60 may cause outer housing 34 (FIG. 1) of IMD 16 to provide asomatosensory alert (e.g., by causing housing 34 of IMD 16 to vibrate ina particular pattern or to just vibrate continuously for a period oftime) in order to provide the notification.

In other examples, processor 60 may be configured to provide anotification by sending a signal, via telemetry module 70, to a remotedevice, from which a clinician or another user may receive thenotification. The remote device may be communicatively linked to IMD 16(or programmer 14) using any suitable system. An example of suitablesystem includes the CareLink Network, available from Medtronic, Inc. ofMinneapolis, Minn., which may include an external device, such as aserver, and one or more computing devices that are coupled to IMD 16 andprogrammer 14 via a network.

Another example of a responsive action that processor 60 may take inresponse to determining the bioelectrical brain signal includes thebiomarker associated with a particular patient state includescontrolling therapy delivery to the patient based on the determinedpatient state. In some examples, if the patient state indicates theoccurrence of a patient symptom or another state in which therapydelivery is desirable, then processor 60 controls stimulation generator64 (FIG. 2) to initiate the delivery of electrical stimulation therapyto patient 12 in response to determining the bioelectrical brain signalincludes the biomarker, and, therefore, in response to detecting thepatient state. Other types of therapy in addition to, or instead of,electrical stimulation therapy are also contemplated.

In another example, if the patient state indicates the occurrence of apatient symptom or another state in which therapy delivery is desirable,processor 60 may modify one or more stimulation parameter values (orother therapy parameter values in the case of therapy other thanelectrical stimulation therapy) with which stimulation generator 64(FIG. 2) generates electrical stimulation therapy in response todetermining the bioelectrical brain signal includes the biomarker, and,therefore, in response to detecting the patient state. For example,processor 60 may determine that the detection of the patient stateindicates the current therapy parameter values implemented bystimulation generator 64 are not effective or could be more effective inmanaging the patient's condition.

In some examples, the biomarker is associated with a movement state. Ifpatient 12 is afflicted with a movement disorder or otherneurodegenerative impairment, then therapy delivery, such as delivery ofelectrical stimulation therapy, a fluid delivery therapy (e.g., deliveryof a pharmaceutical agent), fluid suspension delivery, or delivery of anexternal cue may improve the performance of motor tasks by patient 12that may otherwise be difficult. These tasks may include at least one ofinitiating movement, maintaining movement, grasping and moving objects,improving gait associated with narrow turns, and so forth. Bydetermining when patient 12 is in a movement state, e.g., using thetechnique shown in FIG. 4, therapy system 10 may provide “on demand”therapy to help manage symptoms of the patient's movement disorder.

In some examples, the biomarker is associated with a sleep state. Insome cases, a patient condition, such as Parkinson's disease, may affectthe quality of a patient's sleep. For example, when patient 12 attemptsto sleep, patient 12 may successfully initiate sleep, but may not beable to maintain a certain sleep state (e.g., a nonrapid eye movement(NREM) sleep state). As another example, when patient 12 attempts tosleep, patient 12 may not be able to initiate sleep or may not be ableto initiate a certain sleep state. For example, neurological disordersmay cause patient 12 to have difficulty falling asleep and/or maydisturb the patient's sleep, e.g., cause patient 12 to wakeperiodically. Further, neurological disorders may cause the patient tohave difficulty achieving deeper sleep states, such as one or more ofthe NREM sleep states.

Some patients that are also afflicted with a movement disorder sufferfrom sleep disturbances, such as daytime somnolence, insomnia,disturbances in rapid eye movement (REM) sleep. For example, epilepsy isan example of a neurological disorder that may affect sleep quality.Other neurological disorders that may negatively affect patient sleepquality include movement disorders, such as tremor, Parkinson's disease,multiple sclerosis, or spasticity. The uncontrolled movements associatedwith such movement disorders may cause a patient to have difficultyfalling asleep, disturb the patient's sleep, or cause the patient tohave difficulty achieving deeper sleep states. Further, in some cases,poor sleep quality may increase the frequency or intensity of symptomsexperienced by patient 12 due to a neurological disorder. For example,poor sleep quality has been linked to increased movement disordersymptoms in movement disorder patients.

Automatically delivering therapy to patient 12 in response to detectingthe sleep state by at least detecting the biomarker associated with thesleep state may help alleviate at least some sleep disturbances. Forexample, in some examples, therapy system 10 may deliver stimulation tocertain regions of brain 28 (FIG. 1), such as the locus coeruleus,dorsal raphe nucleus, posterior hypothalamus, reticularis pontis oralisnucleus, nucleus reticularis pontis caudalis, or the basal forebrain,during a sleep state in order to help patient 12 fall asleep, maintainthe sleep state or maintain deeper sleep states (e.g., REM sleep). Inaddition to or instead of electrical stimulation therapy, a suitablepharmaceutical agent, such as acetylcholine, dopamine, epinephrine,norepinephrine, serotonine, inhibitors of noradrenaline or any agent foraffecting a sleep disorder or combinations thereof may be delivered tobrain 28 of patient 12. By alleviating the patient's sleep disturbances,patient 12 may feel more rested, and, as a result, therapy system 10 mayhelp improve the quality of patient's life.

In some examples, the biomarker is associated with a speech state. Somepatients that are also afflicted with a movement disorder suffer fromspeech disorder, such as impaired laryngeal function or articulatorydysfunction. Similarly, in the speech state, patient 12 may successfullyinitiate speech, but may not be able to maintain the verbal fluency,e.g., may unintentionally stop speaking or may have difficulty speaking.As another example, in the speech state, patient 12 may attempt toinitiate speech without success. For example, patients with Parkinson'sdisease may be afflicted with hypokinetic dysarthria, which is a generaldifficulty speaking. It is believed that hypokinetic dysarthria iscaused by dysfunction in the pallidal-cortical and/or thalamocorticalcircuitries, which may result in rigidity and dyskinesia in therespiratory, phonatory, and/or articulatory musculature. Initiating orotherwise adjusting therapy delivery to patient 12 in response todetecting a speech state may help alleviate at least some symptoms of aspeech disorder. For example, in some examples, IMD 16 may deliverstimulation to certain regions of brain 28, such as bilateralstimulation of the subthalamic nucleus or globus pallidus. In additionto or instead of electrical stimulation therapy, a suitablepharmaceutical agent may be delivered to brain 28 of patient 12 to helpmanage speech impairment.

In some examples, if the biomarker is associated with a patient statefor which therapy delivery is not desirable, processor 60 may controlstimulation generator 64 (FIG. 2) to terminate the delivery ofelectrical stimulation therapy to patient 12 in response to determiningthe bioelectrical brain signal includes the biomarker. This responsiveaction may be taken if, for example, processor 60 controls stimulationgenerator 64 to deliver therapy to patient 12 until a patient state inwhich no symptoms or an otherwise positive patient state is achieved.

In addition or instead of the responsive actions discussed above,processor 60 may generate a patient diagnosis based on the determinedpatient state. For example, if patient 12 has been diagnosed with apatient condition and processor 60 detects a particular patient stateassociated with a sub-type of the patient condition, processor 60 maygenerate an indication of the patient condition sub-type in response todetermining the bioelectrical brain signal includes the biomarker. Insome examples, processor 60 may provide an indication of the patientdiagnosis by, for example, transmitting a signal to programmer 14 viathe respective telemetry modules 70, 84, and processor 80 of programmer14 may control a display of user interface 86 to display the patientdiagnosis.

In response to determining the sensed bioelectrical brain signal doesnot include the biomarker (“NO” branch of block 102), processor 60 maycontinue monitoring sensed bioelectrical brain signals for biomarkers.For example, processor 60 may continue receiving a bioelectrical brainsignal (e.g., information representative of the bioelectrical brainsignal) (100) and determining whether the bioelectrical brain signalincludes a biomarker (102). Processor 60 may continue receiving thebioelectrical brain signal (100) at any suitable frequency, which may beregular or irregular, or based on user input (e.g., initiated bypatient, patient caretaker, or clinician input).

In some examples, in the technique shown in FIG. 4, processor 60 selectsthe frequency band of interest, the one or more sub-bands of thefrequency band of interest, or both. Processor 60 may, for example,select the frequency band of interest and determine the one or morefrequency sub-bands of the frequency band of interest that are revealingof the patient state based on biomarker information 76 stored by memory62 (FIG. 2).

As discussed above, different frequency bands of a bioelectrical brainsignal are associated with different brain activity of the patient. Forexample, pathological synchronizations in beta band or other frequencybands of LFPs recorded from electrodes implanted in the basal ganglia ofpatients diagnosed with Parkinson's disease may be a biomarker ofParkinson's disease. For example, activity in a gamma band (e.g., about35 Hz to about 100 Hz) may indicate the movement state of a patient orthe sleep-wake state of a patient. As another example, as shown by thespectrogram illustrated in FIG. 5, the activity in a beta frequency band(e.g., about 13 Hz to about 35 Hz) in patients diagnosed withParkinson's disease may vary as a function of the medicated state of thepatient. For example, the power level in the beta band may decrease whenthe patient is under the influence of medication.

FIG. 5 is an example spectrogram of a bioelectrical brain signal sensedwithin a brain of a human subject. In particular, the bioelectricalbrain signal is a LFP recorded from electrodes implanted in a brain of apatient diagnosed with Parkinson's disease. The graph shown in FIG. 5illustrates the power spectra of the LFPs over approximately 24 hoursand illustrates how much of the sensed bioelectrical brain signal lieswithin each given frequency band over a range of frequencies. The lengthof the LFP recording shown in FIG. 5 permits the therapeutic effects ofa medication taken by the human subject medication) to be observed. They-axis of the spectrogram indicates the frequency of the bioelectricalbrain signal, the x-axis indicates time, and the z-axis, which extendssubstantially perpendicular to the plane of the image of FIG. 5, asindicated by the color of the spectrogram, indicates a power level ofthe bioelectrical brain signal. The spectrogram provides athree-dimensional plot of the signal strength of the frequency contentof a bioelectrical brain signal as it changes over time.

The spectrogram shown in FIG. 5 indicates that high energy activity isobserved in the beta frequency band (e.g., about 13 Hz to about 35 Hz inthe example shown in FIG. 5) with varying amplitudes (as indicated bythe color key shown on the far right side of the spectrogram) dependingon the medication state (e.g., on medication or off medication) of thepatient. FIG. 5 depicts a line graph 110 that indicates the fluctuationof the amplitude of the LFPs in a frequency sub-band (e.g., about 18 Hzto about 28 Hz) of the beta frequency band. The spectrogram shown inFIG. 5 indicates that the amplitude (also referred to herein as the“power level” or “activity”) in at least one frequency sub-band (e.g.,about 18 Hz to about 28 Hz) of the beta frequency band may fluctuate asa function of the medication state of a patient. As a result, one ormore spectral characteristics (e.g., a spectral pattern) of the LFP in aparticular beta frequency sub-band of the beta frequency band mayindicate a patient state in which therapeutic effects of medication areobserved.

FIGS. 6A and 6B are example spectrograms of a bioelectrical brain signalsensed within a brain of a human subject and illustrate the effects ofmedication on activity in a beta frequency band (e.g., about 13 Hz toabout 35 Hz) in a human subject diagnosed with Parkinson's disease. Thesensed bioelectrical brain signal is a LFP recorded from electrodesimplanted in a brain of a patient diagnosed with Parkinson's disease. Aswith the spectrogram shown in FIG. 5, the y-axes of the spectrogramsshown in FIGS. 6A and 6B indicate the frequency band of thebioelectrical brain signal, the x-axes indicates time, and the z-axes,which extend substantially perpendicular to the plane of the respectiveimage, as indicated by the color of the spectrogram, indicate a powerlevel of the bioelectrical brain signal.

In FIG. 6A, during a first time period 112, the human subject is in apathological state and is not under the influence of therapy to mitigateeffects of a movement disorder. As shown in FIG. 6A, in the first timeperiod 112, a power level of the bioelectrical brain signal of the humansubject in a first frequency sub-band (e.g., about 13 Hz to about 20 Hz)of the beta frequency band (e.g., about 13 Hz to about 35 Hz) isrelatively high, as indicated by the relatively intense color 113 inFIG. 6A. In particular, during time period 112, the peak activity in thebeta band occurs in the first frequency sub-band. In a second timeperiod 114, the human subject is under the influence of medication(e.g., a pharmaceutical agent) to mitigate effects of the movementdisorder. As shown in FIG. 6A, compared to the first time period 112,the beta band activity in the first frequency sub-band decreases duringthe second time period 114 in which the human subject is receivingmovement disorder therapy and beta band activity in a second frequencysub-band (e.g., about 20 Hz to about 30 Hz) of the beta band increases.In particular, during time period 114, the peak activity in the betaband 115 occurs in the second frequency sub-band, which is differentthan the first frequency sub-band and does not overlap with the firstfrequency sub-band.

The spectrogram shown in FIG. 6A demonstrates that even though the powerlevel in the overall beta band (e.g., about 13 Hz to about 35 Hz) maynot change significantly, if at all, before and after the onset of theeffects of the medication, the distribution of power between differentsub-bands of the beta band may shift in response to the receipt oftherapy to manage the movement disorder symptoms. This change in theactivity levels in the frequency sub-bands may not be observed if theactivity in the beta band is just observed.

The test results shown in FIG. 6A indicates that a shift in peak powerwithin the beta band between the first and second frequency sub-bands ofthe beta band may be a biomarker for a positive response to medication.It is believed that the shift in peak power of a frequency band ofinterest between two or more sub-bands of the frequency band of interestmay be a biomarker for other patient states instead of or in addition tothe state in which a positive response to medication is observed, suchas, but not limited to, a movement state, a sleep state, a speech state,a state in which one or more symptoms of a patient condition areobserved, and the like.

In FIG. 6B, during a first time period 116, the human subject is in apathological state and is not under the influence of therapy to mitigateeffects of a movement disorder. As shown in FIG. 6B, in the first timeperiod 116, the dominant beta band activity in a first frequencysub-band (e.g., about 13 Hz to about 20 Hz) of the beta frequency band(e.g., about 13 Hz to about 35 Hz) is relatively high, as indicated bythe relatively intense color 117 in FIG. 6B. In particular, during timeperiod 116, the peak activity in the beta band occurs in the firstfrequency sub-band. In a second time period 118, the human subject isunder the influence of medication (e.g., a pharmaceutical agent) tomitigate effects of the movement disorder. As shown in FIG. 6B, comparedto the first time period 116, the dominant beta band activity is in abroader frequency sub-band during the second time period 118 in whichthe human subject is receiving movement disorder therapy. In particular,during time period 118, the dominant activity 119 in the beta bandoccurs in a second frequency sub-band spanning from about 13 Hz to about35 Hz, which is different than the first frequency sub-band, butoverlaps with the first frequency sub-band.

The spectrogram shown in FIG. 6B demonstrates that even though the powerlevel in the beta band may not change significantly, if at all, beforeand after the onset of the effects of the medication, the width of thesub-bands of the beta band that exhibit a relatively highest level ofactivity may change in response to the receipt of therapy to manage themovement disorder symptoms. Again, this change in the activity levels inthe frequency sub-bands may not be observed if the activity in just theoverall the beta band is observed.

The test results shown in FIG. 6B indicate that a change in the width ofa frequency sub-band of the beta band that includes a relatively highestlevel of activity may be a biomarker for a positive response tomedication. It is believed that a change in the width of a frequencysub-band of the beta band that includes a relatively highest level ofactivity may be a biomarker for other patient states instead of or inaddition to the state in which a positive response to medication isobserved, such as, but not limited to, a movement state, a sleep state,a speech state, a state in which one or more symptoms of a patientcondition are observed, and the like.

The test results shown in FIGS. 6A and 6B indicate that a shift in peakpower between the first and second frequency sub-bands of the beta bandmay be a biomarker for a positive response to medication. The peak powercan be indicated by the power level at one frequency or the averagepower level for a plurality of frequencies (e.g., a frequency sub-band).It is believed that the shift in peak power between two or moresub-bands of a frequency band of interest may be biomarker for otherpatient states instead of or in addition to the state in which apositive response to medication is observed (e.g., a movement state, asleep state, a speech state, a state in which one or more symptoms of apatient condition are observed, or the like).

It is believed that for some patients, one or more of a frequencysub-band in which a peak power level (also referred to herein as a peakamplitude) is observed or a spectral pattern (also referred to asoscillatory activity) of a bioelectrical brain signal may be indicativeof a particular patient state. The spectral pattern may be, for example,a morphology of a plot the power level versus frequency (e.g., acharacteristic of a peak of the plot, such as width of the peak or thenumber of modes of the peak), or another pattern with which the powerdistribution between sub-bands of the frequency band shifts.

FIGS. 7A-7D illustrate different (spectral) patterns in oscillatoryactivity that may be indicative of particular patient states (e.g., thesame patient state or different patient states, depending on the patientcondition and the patient). In particular, FIGS. 7A-7D illustrate thepower spectra of LFPs recorded from electrodes implanted in the brainsof human subjects diagnosed with Parkinson's disease. The x-axis of eachof the power spectra shown in FIGS. 7A-7D indicates the frequency of thebioelectrical brain signal, and the y-axis of each power spectraindicates the spectral amplitude (in units of microvolts (μv ms)). Thepower (as indicated by the y-axis value) of the bioelectrical brainsignal at a particular frequency (as indicated by the x-axis value) canbe in units of microvolts squared (μv²), or, as shown in FIG. 7, theroot mean square of the microvolts squared value (referred to in FIGS.7A-7D as μv ms).

FIGS. 7A-7D illustrate frequency-domain graphs of sensed bioelectricalbrain signals having different signal strength distributions. FIG. 7Aillustrates a LFP exhibiting a single signal peak amplitude (alsoreferred to as a “unimodal” peak) in the beta band, where the peak powerlevel occurs in a particular a frequency sub-band of the beta band. FIG.7B illustrates a LFP exhibiting a multiple signal peak amplitudes (alsoreferred to as a “bimodal” or “multimodal” peak) in the beta band, wherethe peak power levels are in different frequency sub-bands of the betaband. In other examples, a bimodal peak indicative of a particularpatient state can include two different peak power levels withindifferent frequency bands in the power spectrum. FIG. 7C illustrates aLFP exhibiting an elevated plateau in a frequency sub-band of the betaband without a clear peak. An elevated plateau may be indicated by, forexample, the power level staying substantially flat or at least notexhibiting a non-exponential fall-off within a particular frequencysub-band, across two or more sub-bands of a frequency band of interest,or even across two or more frequency bands of interest instead ofdecreasing exponentially. FIG. 7D illustrates a LFP exhibiting a peakpower level in the gamma band.

It is believed that, in some cases, the different morphologies shown inFIGS. 7A-7D may be associated with different patient states. Thedifferent morphologies may be characterized by a predeterminedcharacteristic of a signal strength distribution, such as, but notlimited to, one or more of a peak width less than or equal to a firstthreshold value, a peak width greater than or equal to a secondthreshold value, a unimodal peak, or a bimodal peak. In addition, insome examples, a particular morphology indicative of a patient state andstored by IMD 16 as a biomarker can be a particular difference (e.g.,difference in power level) in morphology relative to a baselinebioelectrical brain signal.

Short term recordings of LFPs may not provide sufficient data to comparethe activity of the different frequency sub-bands within the beta band(or gamma band or another frequency band of interest, which may varydepending on the patient condition) to determine how these differentspectral patterns of LFPs (or other bioelectrical brain signals) mayindicate a particular patient state.

Based on longer duration recordings (e.g., on the order of hours or evendays, such as 24 hours) of LFPs, it is believed that not only is thepower level in a particular frequency band (e.g., the beta band or thegamma band) of a sensed bioelectrical brain signal indicative of apatient state, but other spectral characteristics (e.g., indicative ofactivity of a bioelectrical brain signal of a patient in one or morefrequency sub-bands of a frequency band of interest) may be indicativeof a patient state and, in some cases, a more specific patient statethan the power level in the broader frequency band. The longer durationrecordings may, for example, provide a better understanding of thespectral and temporal characteristics of the LFP signals while patentsundergo symptom testing both with and without medication or othertherapy. The other spectral characteristics that may be indicative of apatient state include, for example, the power level of a bioelectricalbrain signal of the patient in one or more frequency sub-bands of thefrequency band, a shift in a power distribution (e.g., a peak power oran average power over a particular range of frequencies) betweensub-bands of the frequency band, a change in the peak frequency withinone or more frequency sub-bands, a characteristic of a distribution of asignal strength within a frequency band (e.g., a narrow peak, a broadpeak, a unimodal peak, or a bimodal peak), or a width or a variability(e.g., in the width), or the one or more frequency sub-bands exhibitinga relatively high or low level of activity.

FIGS. 8A and 8B illustrate characteristics of the beta activity of LFPssensed within a basal ganglia of a brain of a human patient over anapproximately 24 hour period of time. The test results that shown inFIGS. 8A and 8B indicate that, in addition to the changes of LFPamplitude (power level), the LFP spectral patterns may also change as afunction of the patient state. In particular, FIG. 8A illustrates thepeak amplitude (as represented by circles along the bottom plot) of thebeta band activity and the corresponding frequency (as represented bythe crosses along the top plot) at which the peak amplitude occurred.FIG. 8B illustrates a histogram of the beta band frequenciescorresponding to the peaks. The histogram indicates there was a bimodaldistribution of the beta band frequencies corresponding to the peakamplitudes of the sensed LFPs. The probability density functions of twonormal distributions are overlaid in FIG. 8B so that two distinct betafrequency bands can be identified.

The data analysis shown in FIGS. 8A and 8B indicate that the betafrequency band activities in different frequency sub-bands of the betaband (e.g., a low beta sub-band having a frequency range of about 13 Hzto about 20 Hz and a high beta sub-band having a frequency range ofabout 20 Hz to about 30 Hz) may be associated with different componentsof the Parkinson's disease symptoms. This indicates that activity of abioelectrical brain signal of a patient in one or more frequencysub-bands of a frequency band of interest, rather than activity in thebroader frequency band itself, may be used to determine a patient state.The frequency sub-bands of interest (e.g., the sub-bands bands thatcorrespond to normal and pathological brain behaviors, the sub-bandsbands that indicate a patient response medications or other therapies),the power level in a particular frequency sub-band, or another spectralcharacteristic of a bioelectrical brain signal indicative of aparticular patient state may be determined based on relatively long termbioelectrical brain signal recordings from a patient.

The data analysis shown in FIGS. 8A and 8B also indicates that the highand low beta in the case of bimodal peaks may respond different to brainstates as well as to therapies, such that one or more characteristics ofthe bimodal peak may be indicative of a patient state. Thecharacteristics of the bimodal peak can include, for example thefrequency sub-band in which the high beta activity is observed and thefrequency sub-band in which the low beta activity is observed, thefrequency difference between these sub-bands, the power level of thehigh beta activity, the power level of the low beta activity, the ratioof the power levels of the high and low beta activities, the differencein the power levels of the high and low beta activities, and the like.

As indicated above, one or more spectral characteristics of abioelectrical brain signal sensed within particular structures of brain28 of patient 12 and indicative of particular patient states may bestored as a biomarker indicative of a particular patient state. In someexamples, the biomarkers may be determined during a learning phase. Oneexample technique that may be implemented during a learning phase todetermine one or more biomarkers indicative of a particular patientstate is shown in FIG. 9. While the technique shown in FIG. 9, as wellas some other figures are described with respect to processor 80 ofprogrammer 14, in other examples, a processor of another device, such asprocessor 60 of IMD 16 (FIG. 2) can perform any part of the techniquesdescribed herein, alone or in combination with another device.

In accordance with the technique shown in FIG. 9, processor 80 ofprogrammer 14 receives a long term bioelectrical brain signal recording,which indicates a bioelectrical brain signal recorded by sensing module66 (FIG. 2) of IMD 16 (120). The length of the bioelectrical brainsignal recording is selected such that the bioelectrical signal sensedby sensing module 66 during at least two different patient states isrecorded. Processor 80 may receive the long term bioelectrical brainsignal recording (120) from IMD 16 or from another device, such as astorage device.

In addition, processor 80 receives patient state indications thatindicate the occurrence of a particular patient state (122). The patientstate indications correlate in time with the bioelectrical brain signalrecording, such that processor 80 receives both the bioelectrical brainsignal sensed while the patient state was observed and an indication ofthe patient state. Processor 80 can receive the indication of thepatient state via any suitable technique, such as based on input frompatient 12, a patient caretaker, or another person, or based on inputfrom another sensor (e.g., an accelerometer or another type of motionsensor may indicate a movement state). In some examples, patient 12 (oranother user) may interact with user interface 86 of programmer 14 (FIG.3) to provide the input. In other examples, patient 12 (or another user)may provide the input by directly interacting with IMD 16. For example,a motion sensor (e.g., an accelerometer, pressure transducer, gyroscope,or piezoelectric crystal) integrated into or on housing 34 of IMD 16 maybe configured to generate a signal that is indicative of patient 12tapping IMD 14 through the skin. The number, rate, or pattern of tapsmay be associated with different patient states, and processor 80 mayidentify the tapping by patient 12 to determine when patient input isreceived and what type of patient state is indicated by the patientinput.

In the technique shown in FIG. 9, processor 80 generates a spectrogramof the bioelectrical brain signal (124) and determines one or morespectral characteristics indicative of one or more patient states (126).For example, processor 80 may determine (e.g., select) one or morefrequency sub-bands of a frequency band of interest, and determine,automatically or with the aid of a clinician, one or more spectralcharacteristics of the frequency sub-bands of the frequency domain ofthe bioelectrical brain signal that are correlated with the patientstate indication. The characteristic of the frequency sub-band can be,for example, a characteristic that exhibited a change within aparticular time range (e.g., less than about 5 seconds) prior to orafter the patient state indication. In the example shown in FIG. 9, theone or more spectral characteristics determined by processor 80 arestored in memory 82 (or a memory of another device, such as IMD 16 or aremote server) as biomarkers indicative of the associated patient state(128).

Processor 80 may determine one or more frequency sub-bands and one ormore frequency bands of interest using any suitable technique. Forexample, processor 80 may determine the frequency bands and sub-bands(which may be referred to as “functional bands” in some examples) of thefrequency bands that exhibit activity changes within a particular timerange of time relative to the time of the patient state indication. Asanother example, processor 80 may determine one or more frequencysub-bands and one or more frequency bands of interest based on userinput received via user interface 86.

In some examples, after identifying specific frequency sub-bands ofinterest, processor 80 may generate a histogram based on the powerspectra (e.g., as part of a power spectral analysis) to analyze therelative frequency distribution of power in the frequency sub-bands anddetermine patient state biomarkers based on the frequency distribution.In addition, in some examples, processor 80 may perform a coherenceanalysis to cross-compare different biomarkers, and perform acorrelation analysis to determine the relationship between thesebiomarkers and one or more patient states. In this way, processor 80 mayuse statistical measures to automatically determine one or more spectralcharacteristics of a bioelectrical brain signal with which a patientstate may be automatically detected.

By tracking of a bioelectrical brain signal of patient 12 overrelatively long periods of time (e.g., on the order of hours or evendays), characteristics of the one or more frequency sub-band componentsindicative of one or more patient states may be observed. Using thetechnique shown in FIG. 9, processor 80 may perform a power spectralanalysis of the sensed bioelectrical brain signal to determine one ormore biomarkers (e.g., frequency sub-band components) indicative of oneor more patient states.

The one or more biomarkers may facilitate clinical decisions regardingthe patient condition (e.g., to assess the patient condition sub-type),delivery of treatment, such as the selection of a type of therapy (e.g.,drug delivery or electrical stimulation therapy) or the selection of oneor more efficacious therapy delivery parameter values. For example,processor 80, alone or with the aid of a clinician, can determine theeffects of a particular type of therapy, such as a type of medicationtaken by patient 12, electrical stimulation therapy, drug deliverytherapy, or any combination thereof, on spectral bands of abioelectrical brain signal sensed in brain 28 of patient 12 by comparingthe shifts in the location, amplitude, and width of signal peaksrelative to a baseline state (e.g., in which no therapeutic effects ofthe therapy are observed).

In addition or instead, processor 60 of IMD 16, processor 80 ofprogrammer 14 or another processor, alone or with the aid of aclinician, may use the one or more biomarkers to control therapy tomaintain a threshold level of activity in one or more particularfrequency sub-bands or, depending on the patient condition, to maintaina lack of activity in the one or more frequency sub-bands), or toachieve a particular change in the activity in the one or more frequencysub-bands. In this way, the biomarkers may be used by processor 60 ofIMD 16 or another device to control closed-loop therapy.

In other examples of the technique shown in FIG. 9, processor 80 maypresent, via a display of user interface 86, information regarding thespectral components (e.g., frequency sub-bands of interest, spectralpatterns, or both) of a sensed bioelectrical brain signal and a user(e.g., a clinician) may select one or more biomarkers based on thepresented information.

FIG. 10 illustrates a plurality of graphs that each indicates the betaband activity of a LFP sensed within a brain of a human subjectdiagnosed with Parkinson's disease. Graphs corresponding to one of fivehuman subjects are shown in FIG. 10. The x-axis of each graph shown inFIG. 10 indicates the frequency of the LFP sensed within brains of humansubjects and the y-axis indicates the spectral amplitude (also referredto herein as the “power level”) of the activity in the particularfrequency along the x-axis in units of microvolts squared (μv²). Eachrow of graphs in FIG. 10 indicates the LFP sensed in one hemisphere of abrain of a human subject, with each graph indicating a different depthof electrodes. In each row, the graphs from left to right indicate adeeper location within the brain of the subject as the lead was moved inapproximately two millimeter (mm) increments in a deep direction. Foreach subject, a LFP was sensed for a duration of about 60 seconds toabout 90 seconds with implanted electrodes as the lead carrying theelectrodes was implanted to deliver electrical stimulation to thesubthalamic nucleus in the brain of the patient.

The data shown in FIG. 10 indicates that the spectral pattern of the LFPactivity in a brain of a patient over time may depend on the depthwithin the brain of the sense electrodes with which the LFP is sensed.In addition, the graphs in FIG. 10 indicate that different patientsexhibit different beta band activity within frequency sub-bands of thebeta band, as indicated by different spectral patterns (e.g., differentpeak shapes). This may indicate, for example, that different subtypes ofParkinson's disease are associated with different characteristics ofbeta sub-band activity, such as different spectral patterns.

In some examples, programmer 14 or another device, such as IMD 16, isconfigured to determine whether a patient has a particular patientcondition (e.g., Parkinson's disease) based on activity of abioelectrical brain signal of a patient in one or more frequencysub-bands of a frequency band of interest. One or more frequency domaincharacteristics of a bioelectrical brain signal, such as the activity ofa bioelectrical brain signal of a patient in one or more frequencysub-bands of a frequency band of interest, may be markers for thepresence of absence of a particular patient condition. For example,bioelectrical brain signals of patients without a particular patientcondition may not exhibit certain activity in one or more frequencysub-bands of a frequency band of interest, whereas bioelectrical brainsignals of patients with the patient condition may exhibit certainactivity in one or more frequency sub-bands of a frequency band ofinterest. As an example, bioelectrical brain signals of patients that donot have Parkinson's disease may not exhibit certain shifts in powerlevels between two or more frequency sub-bands of a frequency band ofinterest, whereas at least some patients with Parkinson's disease mayexhibit the shifts. Thus, activity in one or more frequency sub-bands ofa frequency band of interest may be indicative of the presence of thepatient condition and may, therefore, be used in some examples todiagnose the patient condition.

The bioelectrical brain signals sensed to diagnose the patient conditionor otherwise determine a patient state may be sensed via implantedelectrodes or via external (e.g., scalp) electrodes.

While the techniques described above are primarily described as beingperformed by processor 60 of IMD 16 or processor 80 of programmer 14, inother examples, one or more other processors may perform any part of thetechniques described herein alone or in addition to processor 60 orprocessor 80. Thus, reference to “a processor” may refer to “one or moreprocessors.” Likewise, “one or more processors” may refer to a singleprocessor or multiple processors in different examples.

The techniques described in this disclosure, including those attributedto IMD 16, programmer 14, or various constituent components, may beimplemented, at least in part, in hardware, software, firmware or anycombination thereof. For example, various aspects of the techniques maybe implemented within one or more processors, including one or moremicroprocessors, DSPs, ASICs, FPGAs, or any other equivalent integratedor discrete logic circuitry, as well as any combinations of suchcomponents, embodied in programmers, such as clinician or patientprogrammers, medical devices, or other devices.

In one or more examples, the functions described in this disclosure maybe implemented in hardware, software, firmware, or any combinationthereof. If implemented in software, the functions may be stored on, asone or more instructions or code, a computer-readable medium andexecuted by a hardware-based processing unit. Computer-readable mediamay include computer-readable storage media forming a tangible,non-transitory medium. Instructions may be executed by one or moreprocessors, such as one or more DSPs, ASICs, FPGAs, general purposemicroprocessors, or other equivalent integrated or discrete logiccircuitry. Accordingly, the term “processor,” as used herein may referto one or more of any of the foregoing structure or any other structuresuitable for implementation of the techniques described herein.

In addition, in some aspects, the functionality described herein may beprovided within dedicated hardware and/or software modules. Depiction ofdifferent features as modules or units is intended to highlightdifferent functional aspects and does not necessarily imply that suchmodules or units must be realized by separate hardware or softwarecomponents. Rather, functionality associated with one or more modules orunits may be performed by separate hardware or software components, orintegrated within common or separate hardware or software components.Also, the techniques could be fully implemented in one or more circuitsor logic elements. The techniques of this disclosure may be implementedin a wide variety of devices or apparatuses, including an IMD, anexternal programmer, a combination of an IMD and external programmer, anintegrated circuit (IC) or a set of ICs, and/or discrete electricalcircuitry, residing in an IMD and/or external programmer.

Various examples have been described. These and other examples arewithin the scope of the following claims.

What is claimed is:
 1. A method comprising: selecting, with one or moreprocessors, a frequency band of interest of a bioelectrical brain signalof a patient from a plurality of predetermined frequency bands includingtwo or more of a delta band, a theta band, an alpha band, a beta band, agamma band, or a high gamma band; after selecting the frequency band ofinterest, selecting, with the one or more processors, two or morefrequency sub-bands from a plurality of frequency sub-bands of theselected frequency band of interest; determining, with the one or moreprocessors, the bioelectrical brain signal includes a biomarkerindicative of a patient state based on stored biomarker informationincluding one or more spectral characteristics of the selected two ormore frequency sub-bands of the selected frequency band of interest; andcontrolling, with the one or more processors, therapy delivery by amedical device to the patient based on determining that thebioelectrical brain signal includes the biomarker indicative of thepatient state.
 2. The method of claim 1, further comprising receivinginformation representative of the bioelectrical brain signal of thepatient.
 3. The method of claim 2, wherein receiving the informationrepresentative of the bioelectrical brain signal comprises receivinginformation representative of the bioelectrical brain signal from asensing module configured to sense the bioelectrical brain signal viaone or more electrodes implanted in a brain of the patient.
 4. Themethod of claim 1, wherein determining the bioelectrical brain signalincludes the biomarker based on the stored biomarker informationincluding one or more spectral characteristics comprises determining thebioelectrical brain signal includes the biomarker based on a spectralpattern of the bioelectrical brain signal in the selected two or morefrequency sub-bands of the selected frequency band of interest.
 5. Themethod of claim 1, wherein the selected two or more frequency sub-bandscomprise a first frequency sub-band of the plurality of frequencysub-bands of the selected frequency band of interest and a secondfrequency sub-band of the plurality of frequency sub-bands of theselected frequency band of interest, and wherein determining thebioelectrical brain signal includes the biomarker based on the storedbiomarker information including one or more spectral characteristicscomprises determining the bioelectrical brain signal includes thebiomarker based on a shift in a power distribution between the firstfrequency sub-band and the second frequency sub-band over time.
 6. Themethod of claim 1, wherein determining the bioelectrical brain signalincludes the biomarker based on the stored biomarker informationincluding one or more spectral characteristics comprises determining thebioelectrical brain signal includes the biomarker based on a change in apeak frequency within the selected two or more frequency sub-bands ofthe selected frequency band of interest.
 7. The method of claim 1,wherein determining the bioelectrical brain signal includes thebiomarker based on the stored biomarker information including one ormore spectral characteristics comprises determining the bioelectricalbrain signal includes the biomarker based on a predeterminedcharacteristic of a distribution of a signal strength within theselected two or more frequency sub-bands of the selected frequency bandof interest of the bioelectrical brain signal.
 8. The method of claim 7,wherein the predetermined characteristic of the distribution comprisesone or more of a bimodal peak, a peak width less than or equal to afirst threshold value, a peak width greater than or equal to a secondthreshold value, or a unimodal peak.
 9. The method of claim 1, whereindetermining the bioelectrical brain signal includes the biomarker basedon the stored biomarker information including one or more spectralcharacteristics comprises determining the bioelectrical brain signalincludes the biomarker based on a width or a variability of the two ormore frequency sub-bands of the selected frequency band of interestexhibiting a relatively high or low level of activity.
 10. The method ofclaim 1, further comprising generating an indication in response todetermining the bioelectrical brain signal includes the biomarker. 11.The method of claim 1, further comprising generating a patient diagnosisbased on determining the bioelectrical brain signal includes thebiomarker.
 12. The method of claim 1, wherein controlling therapydelivery by the medical device comprises controlling delivery ofelectrical stimulation therapy to a brain of the patient based ondetermining that the bioelectrical brain signal includes the biomarkerindicative of the patient state.
 13. The method of claim 12, wherein theelectrical stimulation therapy is configured to manage at least one of amovement disorder or a seizure disorder of the patient.
 14. The methodof claim 1, wherein controlling therapy delivery by the medical deviceto the patient comprises at least one of modifying one or more parametervalues of the therapy, initiating delivery of the therapy, orterminating delivery of the therapy.
 15. The method of claim 1, whereinat least two frequency sub-bands of the selected two or more frequencysub-bands of the selected frequency band of interest do not overlap witheach other.
 16. The method of claim 1, wherein: the selected frequencyband of interest is the beta band; and a first frequency sub-band of theselected two or more frequency sub-bands of the selected frequency bandof interest includes higher frequencies of the beta band relative to asecond frequency sub-band of the selected two or more frequencysub-bands of the selected frequency band of interest.
 17. The method ofclaim 16, wherein determining the bioelectrical brain signal includesthe biomarker based on the stored biomarker information including one ormore spectral characteristics comprises determining whether a dominantamount of beta band activity is in the first frequency sub-band or thesecond frequency sub-band.
 18. The method of claim 16, wherein thebiomarker is one of a plurality of biomarkers including at least a firstbiomarker and a second biomarker, and wherein determining thebioelectrical brain signal includes the biomarker based on the storedbiomarker information including one or more spectral characteristicscomprises: determining whether the bioelectrical brain signal includesthe first biomarker based on a dominant amount of beta band activitybeing in the first frequency sub-band; and determining whether thebioelectrical brain signal includes the second biomarker based on thedominant amount of beta band activity being in the second frequencysub-band.
 19. The method of claim 16, wherein determining thebioelectrical brain signal includes the biomarker based on the storedbiomarker information including one or more spectral characteristicscomprises detecting a shift in a peak amplitude of activity from thefirst frequency sub-band to the second frequency sub-band.
 20. Themethod of claim 1, wherein the biomarker is one of a plurality ofbiomarkers including at least a first biomarker and a second biomarker,and wherein determining the bioelectrical brain signal includes thebiomarker based on the stored biomarker information including one ormore spectral characteristics comprises: determining whether thebioelectrical brain signal includes the first biomarker based on one ormore first spectral characteristics of the selected two or morefrequency sub-bands of the selected frequency band of interest; anddetermining whether the bioelectrical brain signal includes the secondbiomarker based on one or more second spectral characteristics,different than the one or more first spectral characteristics, of theselected two or more frequency sub-bands of the selected frequency bandof interest.
 21. The method of claim 1, wherein the one or more spectralcharacteristics include one or more of a power level in one or morefrequency sub-bands of the selected two or more frequency sub-bands or aspectral pattern including a pattern in a power distribution between theselected two or more frequency sub-bands over time.
 22. The method ofclaim 1, wherein the patient state comprises one or more of a movementstate, a speech state, or a sleep state.
 23. The method of claim 1wherein each of the selected two or more frequency sub-bands is narrowerthan the selected frequency band of interest.
 24. The method of claim 1,wherein the medical device comprises an implantable medical device. 25.A system comprising: a memory that stores biomarker information; and oneor more processors configured to: select a frequency band of interest ofa bioelectrical brain signal of a patient from a plurality ofpredetermined frequency bands including two or more of a delta band, atheta band, an alpha band, a beta band, a gamma band, or a high gammaband; after selecting the frequency band of interest, select two or morefrequency sub-bands from a plurality of frequency sub-bands of theselected frequency band of interest; determine the bioelectrical brainsignal includes a biomarker indicative of a patient state based on thestored biomarker information, wherein the stored biomarker informationincludes one or more spectral characteristics of the selected two ormore frequency sub-bands of the selected frequency band of interest; andcontrol a medical device to deliver therapy to the patient based ondetermining that the bioelectrical brain signal includes the biomarkerindicative of the patient state.
 26. The system of claim 25, furthercomprising: the medical device, wherein the medical device comprises animplantable medical device; and a sensing module configured to sense thebioelectrical brain signal of the patient.
 27. The system of claim 25,wherein, to determine the bioelectrical brain signal includes thebiomarker based on the stored biomarker information including one ormore spectral characteristics, the one or more processors are configuredto determine the bioelectrical brain signal includes the biomarker basedon a spectral pattern of the bioelectrical brain signal in the selectedtwo or more frequency sub-bands of the selected frequency band ofinterest.
 28. The system of claim 25, wherein the selected two or morefrequency sub-bands comprise a first frequency sub-band of the pluralityof frequency sub-bands of the selected frequency band of interest and asecond frequency sub-band of the plurality of frequency sub-bands of theselected frequency band of interest, and wherein, to determine thebioelectrical brain signal includes the biomarker based on the storedbiomarker information including one or more spectral characteristics,the one or more processors are configured to determine the bioelectricalbrain signal includes the biomarker based on a shift in a powerdistribution between the first frequency sub-band and the secondfrequency sub-band over time.
 29. The system of claim 25, wherein, todetermine the bioelectrical brain signal includes the biomarker based onthe stored biomarker information including one or more spectralcharacteristics, the one or more processors are configured to determinethe bioelectrical brain signal includes the biomarker based on a changein a peak frequency within the selected two or more frequency sub-bandsof the selected frequency band of interest.
 30. The system of claim 25,wherein, to determine the bioelectrical brain signal includes thebiomarker based on the stored biomarker information including one ormore spectral characteristics, the one or more processors are configuredto determine the bioelectrical brain signal includes the biomarker basedon a predetermined characteristic of a distribution of a signal strengthwithin the selected two or more frequency sub-bands of the selectedfrequency band of interest of the bioelectrical brain signal.
 31. Thesystem of claim 30, wherein the predetermined characteristic of thedistribution comprises one or more of a bimodal peak, a peak width lessthan or equal to a first threshold value, a peak width greater than orequal to a second threshold value, or a unimodal peak.
 32. The system ofclaim 25, wherein, to determine the bioelectrical brain signal includesthe biomarker based on the stored biomarker information including one ormore spectral characteristics, the one or more processors are configuredto determine the bioelectrical brain signal includes the biomarker basedon a width or a variability of the two or more frequency sub-bandsexhibiting a relatively high or low level of activity.
 33. The system ofclaim 25, wherein the one or more processors are configured to generatean indication in response to determining the bioelectrical brain signalincludes the biomarker.
 34. The system of claim 25, wherein the one ormore processors are configured to generate a patient diagnosis based ondetermining the bioelectrical brain signal includes the biomarker. 35.The system of claim 25, wherein, to control therapy delivery by themedical device, the one or more processors are configured to controldelivery of electrical stimulation therapy to a brain of the patientbased on determining that the bioelectrical brain signal includes thebiomarker indicative of the patient state.
 36. The system of claim 35,wherein the electrical stimulation therapy is configured to manage atleast one of a movement disorder or a seizure disorder of the patient.37. The system of claim 25, wherein, to control the therapy delivery bythe medical device to the patient, the one or more processors areconfigured to at least one of modify one or more parameter values of thetherapy, initiate delivery of the therapy, or terminate delivery of thetherapy.
 38. The system of claim 25, wherein at least two frequencysub-bands of the selected two or more frequency sub-bands of theselected frequency band of interest do not overlap with each other. 39.The system of claim 25, wherein: the selected frequency band of interestis the beta band; and a first frequency sub-band of the selected two ormore frequency sub-bands of the selected frequency band of interestincludes higher frequencies of the beta band relative to a secondfrequency sub-band of the selected two or more frequency sub-bands ofthe selected frequency band of interest.
 40. The system of claim 39,wherein, to determine the bioelectrical brain signal includes thebiomarker based on the stored biomarker information including one ormore spectral characteristics, the one or more processors are configuredto determine whether a dominant amount of beta band activity is in thefirst frequency sub-band or the second frequency sub-band.
 41. Thesystem of claim 39, wherein the biomarker is one of a plurality ofbiomarkers including at least a first biomarker and a second biomarker,and wherein, to determine the bioelectrical brain signal includes thebiomarker based on the stored biomarker information including one ormore spectral characteristics, the one or more processors are configuredto: determine whether the bioelectrical brain signal includes the firstbiomarker based on a dominant amount of beta band activity being in thefirst frequency sub-band; and determine whether the bioelectrical brainsignal includes the second biomarker based on the dominant amount ofbeta band activity being in the second frequency sub-band.
 42. Thesystem of claim 39, wherein, to determine the bioelectrical brain signalincludes the biomarker based on the stored biomarker informationincluding one or more spectral characteristics, the one or moreprocessors are configured to detect a shift in a peak amplitude ofactivity from the first frequency sub-band to the second frequencysub-band.
 43. The system of claim 25, wherein the biomarker is one of aplurality of biomarkers including at least a first biomarker and asecond biomarker, and wherein, to determine the bioelectrical brainsignal includes the biomarker based on the stored biomarker informationincluding one or more spectral characteristics, the one or moreprocessors are configured to: determine whether the bioelectrical brainsignal includes the first biomarker based on one or more first spectralcharacteristics of the selected two or more frequency sub-bands of theselected frequency band of interest; and determine whether thebioelectrical brain signal includes the second biomarker based on one ormore second spectral characteristics, different than the one or morefirst spectral characteristics, of the selected two or more frequencysub-bands of the selected frequency band of interest.
 44. The system ofclaim 25, wherein the one or more spectral characteristics include oneor more of a power level in one or more frequency sub-bands of theselected two or more frequency sub-bands or a spectral pattern includinga pattern in a power distribution between the selected two or morefrequency sub-bands over time.
 45. The system of claim 25, wherein thepatient state comprises one or more of a movement state, a speech state,or a sleep state.
 46. The system of claim 25, wherein each of theselected two or more frequency sub-bands is narrower than the selectedfrequency band of interest
 47. A non-transitory computer readablestorage medium comprising instructions that, when executed by one ormore processors, cause the one or more processors to: select a frequencyband of interest of a bioelectrical brain signal of a patient from aplurality of predetermined frequency bands including two or more of adelta band, a theta band, an alpha band, a beta band, a gamma band, or ahigh gamma band; after selecting the frequency band of interest, selecttwo or more frequency sub-bands from a plurality of frequency sub-bandsof the selected frequency band of interest; determine the bioelectricalbrain signal includes a biomarker indicative of a patient state based onstored biomarker information including one or more spectralcharacteristics of the selected two or more frequency sub-bands of theselected frequency band of interest; and control therapy delivery by amedical device to the patient based on determining that thebioelectrical brain signal includes the biomarker indicative of thepatient state.